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Multidisciplinary and Historical Perspectives for Developing Intelligent and Resource-Efficient Systems

Aarne Mämmelä1, Senior Member, IEEE, Jukka Riekki2, Member, IEEE, Adrian Kotelba1, Senior Member, IEEE, and Antti Anttonen1, Senior Member, IEEE 1VTT Technical Research Centre of Finland Ltd, P.O. Box 1100, FI-90571 Oulu, Finland 2University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Corresponding author: Aarne Mämmelä (e-mail: Aarne.Mammela@vtt.fi).

The work reported in this paper was partially supported by the European H2020 Research and Innovation project COHERENT (GA No. H2020-ICT-671639).

The work was also conducted in the framework of the self-funded Future Communications Growth Area project and the AFORM5 project of the VTT

Technical Research Centre of Finland Ltd.

ABSTRACT As communication and computation systems become more complex and target at higher

performance, the fundamental limits of nature can be expected to constrain their development and

optimization. This calls for intelligent use of basic resources, that is, materials, energy, information, time,

frequency, and space. We present a multidisciplinary and historical review on the body of knowledge that

can be applied in researching such intelligent and resource-efficient systems. We review general system

theory, decision theory, control theory, computer science, and communication theory. While

multidisciplinarity has been recognized important, there are no earlier reviews covering all these five

disciplines. Based on the review, we build a chronology of intelligent systems and identify connections

between the disciplines. Optimization, decision-making, open- and closed-loop control, hierarchy, and degree

of centralization turn out as recurring themes in these disciplines, which have converged to similar solutions

that are based on remote control, automation, autonomy, and self-organization. We use future wireless

networks as an example to illustrate the open questions and how they can be addressed by applying

multidisciplinary knowledge. This paper can help researchers to use knowledge outside their own field and

avoid repeating the work done already. The resulting consolidated view can speed up research and is

especially important when the fundamental limits of nature are approached and new insights are required to

overcome the challenges. The general, long-standing problem to be tackled is multiobjective optimization

with autonomous and distributed decision-making in an uncertain, dynamic, and nonlinear environment

where the objectives are mutually conflicting.

INDEX TERMS Autonomous systems, fundamental limits, multidisciplinary view, resource efficiency

I. INTRODUCTION

We present a multidisciplinary review of reviews of

intelligent technologies and resource efficiency, which are

seen crucial in 2010-2050, but set mutually conflicting

requirements [1], [2], [3]. Wilenius [2] refers to this period

and states: “This emerging new wave of development calls

for intelligent use of resources.” We argue that a

multidisciplinary approach is required in researching

intelligent resource usage because the fundamental limits of

nature are expected to constrain the development of

communication and computation systems when these

systems become more complex and target at higher

performance. Moreover, analytical thinking leading to

specialized solutions and systems is not sufficient alone, but

rather systems thinking and more general solutions are

required. The rest of the introduction presents justification

for multidisciplinary studies, introduces the basic concepts,

and describes the contributions and structure of this paper.

A. MULTIDISCIPLINARY VIEW

Scientific inquiry has been successfully carried out in a

compartmentalized manner in specialized disciplines. We

have surveyed the earlier studies [4], [5], [6] and identified

five disciplines that can contribute to the research of

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intelligent and resource-efficient systems. These disciplines

are general system theory [7], [4], decision theory [8], [9],

control theory [10], [11], [12], computer science [13], [6],

and communication theory [14], [15], [16]. Moreover, we

include electronics in general system theory as a method to

connect ideas to reality and to study implementation

complexity. The purpose of general system theory is to

gather results from different disciplines.

Decision theory has been developed in operations

research, management theory, and economics [4], [17], [6].

The theory is used in various disciplines and is especially

relevant when we consider the decisions in the control loop

in Fig. 1a). A human, who acts as a decision maker in the

remote control station on the left, receives real-time sensing

information (for example, location, direction, and speed)

from a ship on the right and is therefore able to control it

remotely. The control or feedback loop is generalized in Fig.

1b where the decision maker is replaced with a decision

block (Decide), a fundamental element in many automata.

The decision blocks may form a hierarchy and exchange

sensing and control information with the upper level decision

blocks. The process or the system to be controlled cannot be

accurately modeled and therefore it is usually assumed to

include some noise.

FIGURE 1. A control loop. a) An example where a ship is remotely controlled. b) A general control loop where the system to be controlled is called a process. Solid lines with arrows denote control signals and dashed lines with arrows denote sensing signals. The sense, decide, and act blocks close the loop through the process that is often called the plant.

Humans can participate in the decision-making of many

intelligent and resource-efficient systems. Due to advances

in science and technology, humans can have wider

awareness of their environment, even globally, and actual

presence is no more needed. The awareness can in the future

be expanded in the form of telepresence where all the senses

(for example, sight, hearing, and sense of touch) come into

use and the delays are minimized, so that the interactions

with the environment look almost instantaneous and an

operator feels physically present at the remote site [21], [22].

In virtual reality, the user has the impression of being

physically present in an imaginary environment instead of

actual presence.

Multidisciplinary studies of the five disciplines are still

rare [18], [19] and they usually have rather narrow scopes.

On the other hand, general books such as [7], [20], [4] have

much wider scopes. Books on systems thinking [4] cover the

history only until about 1980. In addition, the general books

[7], [20] need an update as most of the novel concepts

covered by our studies are excluded from these books and

the focus in [7] is in mechanical engineering. Ogata [10]

presents an excellent summary about control engineering but

does not discuss hierarchical, distributed, learning,

autonomous, and self-organizing control. Saridis [5]

combines the results of artificial intelligence (AI), operations

research, and control theory and uses them in intelligent

hierarchical control.

Control theory, computer science, and communication

theory have so far progressed rather independently although

they show similar trends, including the use of hierarchy and

feedback, especially in autonomous and self-organizing

systems. Control theory has often been leading the

development, sometimes by decades. The most advanced

concepts in control theory, computer science, and

communication theory are presented in the simplified

chronology in Fig. 2. A more extensive chronology is

presented in Section II. We observe that in control theory the

most advanced ideas are autonomy and remote control over

a network, whereas in computer science, the idea is

autonomy in self-organizing distributed computing, and in

communications, the idea is autonomy in a self-organizing

network. These terms are explained in Section II.

FIGURE 2. A simplified chronology of the most advanced systems that combine efforts from control theory, computer science, and communication theory.

Control theory, computer science, and communication

theory form a triplet that is merged in automatic,

autonomous, and networked control systems and using

different terms such as cyber-physical systems [23] and time

sensitive networks [24] (Fig. 2). Robotics is an area which

has used the results of different disciplines extensively,

including ideas of intelligent agents [25], [26]. Originally,

Wiener (1948) combined control and communications in his

cybernetics [4]. Autonomic computing combines control

theory and computer science [27]; remote control or

teleoperation combines control theory and communication

theory [28]; and distributed computing combines computer

science and communications [29]. Tactile Internet [30], [31],

[32] is an idea that has been developed in control theory for

many decades since about 1945 in the form of teleoperation

or remote control, networked control systems, and

kinesthetic and haptic feedback [33], [34], [35], [36].

Without a systems view connecting these disciplines

together, reinventions of similar concepts might happen also

in the future.

Multidisciplinary research covering the five disciplines

would facilitate accessing knowledge outside each

researcher’s own field and avoid repeating the work already

done. Moreover, multidisciplinary thinking enables the

emergence of new scientific principles and opportunities as

well [37]. On the other hand, multidisciplinary studies are

challenging since the amount of literature is increasing

exponentially, doubling every 10-15 years depending on the

discipline. Thus, historical reviews become important.

A map of the world can be used to illustrate the role of

multidisciplinary research. The map is based on the efforts

of thousands of cartographers. Such knowledge is not meant

to replace the more detailed knowledge attained by

specialists, but to evidence the relations of each part to other

parts and to the whole to unify, and to organize. This

example was originally presented by the founder of the

discipline of history of science, George Sarton (1884-1956)

[38]. He was interested in natural and human sciences,

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including social sciences, and his goal was to create a

philosophy of science with the unity of science as a core idea.

Sarton’s predecessor was William Whewell (1794-1866).

Conventionally, in engineering, only natural sciences have

been seen important, but also human sciences have

connections to engineering, in studying the needs and use of

technology, and social sciences as well [39].

Some hierarchical systems include a phenomenon called

emergence [4]. It means that the upper level system has

properties that are not predictable from the lower levels.

Complexity in general calls for system studies, to find

common goals to experts [40]. Multidisciplinary view is an

additive view connected to systems thinking [41], [42]. The

basic tools supporting systems thinking are bibliographies

showing available reviews, books, and original papers,

chronologies presenting history, and conceptual analysis in

the form of classification or taxonomies and hierarchies.

Understanding the history is a prerequisite for systems

thinking. Knowing the history helps to understand better the

state of the art and the future trends. Furthermore, conceptual

analysis is needed to find unified terminology for

communication between different disciplines [7]. This may

imply that some disciplines change their definitions.

The multidisciplinary view is, however, only the first step

towards a holistic top-down systems view, which is the

opposite of reductive analytical bottom-up view [41], [42].

Interdisciplinary view is a more advanced, interactive view

and the third and most advanced, transdisciplinary view is

the holistic systems view. Hence, this paper presents our first

step towards the systems view. The term “analytical view” is

commonly used to refer to the opposite view leading to

specialized solutions and systems, but this terminology

includes a small inconsistency since “analysis” is also used

in systems view [41]. The difference is that in the systems

view, a system is observed as a whole and as part of the

environment, whereas in the analytical view, the focus is on

the parts of the system isolated from each other and from the

environment. In the systems view, all the approaches that can

maximize the system performance are considered in order to

achieve a good balance, for example, between computation

and communication. The best practices from all the

disciplines can be used. Due to emergence, the reductive

analytical view cannot produce as extensive knowledge as

the holistic systems view.

Our paper is about linking disciplines together, not about

the details in individual disciplines. The details are covered

in the references. The output of interdisciplinary scientific

research can be measured [43]. The top 1% most cited papers

exhibit higher levels of interdisciplinarity than other papers

in other citation rank classes [44]. Thus, interdisciplinary

research plays an important role in generating high impact

knowledge. However, in some disciplines, editors tend to

prefer contributions directly related to new methods and

algorithms rather than on results of applications from

existing methods [45].

B. BASIC CONCEPTS

Future communication and computation systems need to be

intelligent, that is, they need to have the ability to act

appropriately in an uncertain environment [46], [6].

Appropriate actions advance the goal of the system and

uncertainty means lack of precise knowledge [8]. A system

is manually controlled if a human is in the decision loop (Fig.

3). Some manually controlled systems are remotely

controlled manipulators [22]. A system is automatic if it

works without human intervention but uses some

predetermined control information, for example an external

control signal (Fig. 1b) called the set-point value or a

reference signal that may represent the route to the goal [10].

If there is an obstacle, an automatic system stops and waits

until the obstacle is removed. An automatic system is

autonomous or self-governing if it can achieve its goal

without any external control [12], [47], [48], [49]. An

autonomous system defines its own route to the goal, and if

there is an obstacle, the system is able to find a new route.

Self-organizing systems are advanced forms of autonomous

systems, able to change their own structure.

FIGURE 3. Manually-controlled, automatic, and autonomous systems. A self-organizing system is an advanced form of an autonomous system.

The term autonomic has been earlier used only for

autonomic nervous systems implying unconscious control.

Some disciplines, especially computing, started to use the

term autonomic in about 2001 [50], and the usage spread to

other disciplines, for example communication and control

[51], [52]. Authors have different opinions about the

meaning of the term. The consensus seems to be that

autonomic systems are more advanced than autonomous

systems. Autonomic computing systems “can manage

themselves, given high-level objectives from administrators”

[50]. They are self-managing, i.e., they have self-

configuring, self-optimizing, self-healing, self-monitoring,

and self-protecting properties [50], [47]. In [52], the authors

explain that contrary to autonomous systems, autonomic

systems are cooperating using a dedicated communication

channel. We prefer the general terms cooperative and self-

organizing systems.

Generally, a system is defined to be a set of parts with

some relationships so that the parts form a whole which has

properties of the whole rather than properties of the parts [4].

A system is an object of study whose boundaries are defined

by its observer [20]. When several disciplines meet, special

care is needed even with the basic concepts like the system

being studied. When a part of a larger system is studied, the

boundaries drawn by the observer determine which parts of

the larger system belong to the system being studied and

which parts belong to its environment. For example, in

artificial intelligence, the focus is typically on an agent

interacting with its environment via sensors and actuators

[6], whereas control theory focuses on controlling a plant or

a process in the environment that is included in the feedback

loop [10]. The same entity can be described in both ways, but

the observed boundaries can differ. We follow the

conventions of control theory.

4

According to [4], control, communication, emergence,

hierarchy, and open systems are essential concepts in

systems thinking. We also consider resource-efficiency an

important concept. A general control, learning, or decision

loop or cycle based on feedback is a core component of any

intelligent and resource-efficient system and a unifying

concept for the five disciplines covered in this survey. After

the stability analysis of the feedback loop [53], the learning

loop was used by Dewey, Lewin, and Piaget whose work

lead to Kolb’s concept on experiential learning [54]. Eykhoff

(1960) used the loop to describe the process of learning in

adaptive systems [55]. The operational phases of the loop

were measurement, data reduction or learning, decision, and

adjustment. The loop has been proposed independently many

times with different names such as sense-plan-act paradigm

in robotic systems [26]; observe, orient, decide, and act loop

(OODA) in combat operations process [56]; decision-

making process in situation awareness [57]; cognition or

cognitive cycle in cognitive radios [58], [59]; and monitor,

analyze, plan, execute, and knowledge loop (MAPE-K) in

autonomic computing [50].

All agents are based on a control loop [60], [25], [6]. A

human agent has sensors (e.g., eyes, ears, and skin) and

actuators (e.g., hands, legs, and vocal tract). A robotic agent

may have cameras, infrared range finders, and various

motors. In social sciences, autonomous agents are called

actors which are usually humans [41]. Such agents and

actors show rational goal-directed behavior [46], [6]. In a

multiagent system, the goals of individual agents may be

mutually conflicting. Hence, if cooperation is not organized,

competition may lead to unstable or otherwise less optimal

behavior. In fact, as discussed above, many different control

loops can be studied when system borders outline different

parts of the agent and place the rest of the agent into the

environment. The highest level loop outlines the whole agent

from its environment and is usually the focus in artificial

intelligence. When the studied system covers smaller parts

of the agent, control theory is typically applied, for example,

to turn the camera of a mobile robot or to control its heading

direction. Communication theory enters the stage when the

agent components are distributed in space and additional

control loops are needed for communication. Hierarchies

(discussed below) can be used to relate the different control

loops with each other.

Agents can be divided into hardware and software agents

depending on the implementation [25], [26], [61]. In

applications, agents can also be divided into autonomous

agents, cooperating multiagent systems, and assistant agents

assisting humans. According to the architecture, agents are

divided into reactive, proactive or deliberative, and

interacting or social agents and their mixed or hybrid forms.

Interacting agents can be either competitive or cooperative.

The goal of the control loop is to improve the

performance, that is, to achieve the desired plant behavior

while using resources efficiently. The key performance

indicators (KPIs) are often efficiency metrics demonstrating

the efficient use of resources. Metrics are called criteria or

objective functions. Each objective function may have a

constraint and a goal and those goals may be conflicting [62].

Objective functions, constraints, and goals are treated in [62]

as design metrics. We assume that objective, criterion, and

metric are synonymous terms. Optimization is moving from

the conventional single-objective optimization to

multiobjective optimization where the set of Pareto optimal

solutions is an essential concept [62]. The optimum is not

unique but shows the needed trade-offs. Intervention of a

decision maker is needed to make the final decision in

multiple-criteria decision-making; fairness must be

considered as well. In self-organizing systems, the structure

of the plant can be changed in addition to its behavior.

Stability is an essential property for the feedback loop. In

general, negative feedback is used, and a delay in the loop

may have harmful effects to the stability.

Complex systems are also called large-scale systems and

they are usually hierarchical [63]. In such a case, the set-

point value or the reference signal of a decision block can be

generated as an action by a decision block at an upper level

in the hierarchy; on the other hand, a decision block may

provide some sensing information to the upper level

(Information exchange in Fig. 1b). Hierarchical systems

combine centralized and decentralized control, often in a

mixed form called distributed control [29], [64].

Fundamental limits of nature set constraints for complex

communication and computation systems that target at high

performance. We are quickly approaching many different

fundamental limits of nature because of exponential trends

in requirements such as bit rate and delay [65]. These trends

have been possible because of miniaturization of electronics.

According to Moore’s prediction in 1975 (usually called

Moore’s law), the number of transistors on a chip has been

doubled every second year and this will continue until about

2021 [66], [67]. According to Keyes’ prediction, the

switching energy of a transistor has reduced by a factor of

100 every ten years until about 2000 after which there has

been a slowdown [66]. Understanding of the fundamental

limits of nature is indispensable to obtain high resource

efficiency when the resources are scarce [40], [4], [69]. We

explain also the fundamental limits relevant to

communication systems, whereas in [69], the limits were

presented only for computing.

Although surpassing the limits is being discussed (e.g.,

delays could be reduced to zero in the quantum Internet

[70]), our plan is to approach these limits with intelligent

management of the basic resources. We derive the basic

resources directly from the open system concept. Open

systems are such that their boundaries are crossed by

materials (mass), energy (power), and information (data and

control) [71], [7], [4]. In university physics, only closed

systems are usually considered although all technical

systems are open. Conventionally, the basic resources in

communications have included only energy and bandwidth

[72].

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C. NEW CONTRIBUTION

Our contribution to the state of the art is the following: We

present an extensive multidisciplinary bibliography of earlier

reviews and books. This bibliography includes over 200

carefully selected books and review papers searched with the

keywords intelligence (a method to manage uncertainty in

complex environments), feedback (an enabler for learning

and iterative problem solving), hierarchy (a method to

manage complexity), distributed systems (a result from the

spatial extent of a network), and resource efficiency (a metric

to assess how well the basic resources are used to produce

the desired result). With the original papers, the total number

of references exceeds 400.

We show that the ideas have progressed partially

independently in different disciplines as demonstrated by the

comprehensive chronology covering the last hundred years.

We present a classification of hierarchical and distributed

systems using the early results of control theory. Using the

hierarchy of natural systems [73], we present a modern

hierarchy of human-made systems with increasing

uncertainty and decision complexity. Our hierarchy includes

static, simple dynamic, control, adaptive, learning, and self-

organizing systems. Using the open system concept, we

explain the relationships between the basic resources that are

materials, energy, information, time, frequency, and space.

The fundamental limits and the ideas to treat them are

summarized. We show that in many cases we are

approaching the limits and because of many conflicting

goals, multiple-criteria decision-making will become

mandatory to obtain improvement.

The bibliography, chronology, and conceptual analysis

provide a good starting point for a researcher who is

developing intelligent and resource-efficient systems and

evaluating whether the multidisciplinary systems approach

could be beneficial in the research being conducted. We use

future wireless networks as an example to illustrate the open

questions related to intelligent and resource-efficient

systems and how they can be addressed by applying

multidisciplinary knowledge. Wireless networks pose an

exceptional challenge for control since they are complex,

distributed in space, and the environment is rapidly changing

due to mobility.

Open problems of science were discussed by Bois-

Raymond in 1880 and they are still relevant [40]. Many of

them are related to emergence. Among them are

morphogenesis or self-organization [71] and consciousness

that is characterized by sensation, emotion, volition, and

thought [74]. Artificial consciousness including emotion and

volition is an active area of contemporary research [75], [76].

We focus on the cognition part of consciousness, including

the problems of sensing, deciding, and self-organization, and

exclude the problems of emotion and volition, implying free

and independent decisions, from this survey.

D. STRUCTURE OF THE PAPER

The paper is organized as follows. In Section II, we

present the major general reviews in different related

disciplines and the relevant definitions. We emphasize the

historical development of the disciplines by presenting a

chronology covering the last hundred years. In Section III,

we summarize the observed connections between the

disciplines. In Section IV, we specifically apply the state-of-

the-art results to wireless communication systems. Finally,

Section V includes our conclusions.

II. LITERATURE AND CHRONOLOGY OF INTELLIGENT RESOURCE-EFFICIENT SYSTEMS

Earlier reviews and books are listed in Table I using the

classification presented in the introduction. Due to the wide

scope of the review, we could select only a small fraction of

the literature available, and such selections are always

somewhat subjective. Quality was the main selection

criterion. Papers with clear conceptual analysis and broad

historical reviews were emphasized. The newest review

papers and books were preferred. Some reviews cover a

rather short time span; hence, some old reviews were

selected to provide complementary information. Journal and

magazine papers were favored over conference papers and

reports. The papers matching more than one category were

classified based on their main contribution.

TABLE I. Reviews and books

In Fig. 4, we present a chronology of intelligent systems

based on our review. We use the same division of disciplines

as in Table I but add some original papers. The concepts

presented in Fig. 4 are somewhat different from those in

Table I since our purpose is show explicitly the trends. The

details can be found from the references given in Table I and

elsewhere in this review.

FIGURE 4. Chronology of intelligent resource-efficient systems. The concepts and acronyms are explained in the text. The date of the first occurrence of a concept in the literature is indicated with the position of the first letter of each term.

In addition to the common divergence in research, there

is an opposite convergence trend, which is seen for example

in networked control systems. We can also see the general

trend towards more intelligent systems, though different

terminology is being used for similar concepts.

A. GENERAL SYSTEM THEORY

The history of general system theory starts in the antique [4],

but systems thinking is basically an idea of the last century

[20], originally started by Bertalanffy in a seminar

presentation in 1937 and in a written form in 1945 [71]. He

also presented the idea of open systems in 1940. A precursor

of Bertalanffy’s work was Alexander Bogdanov’s (1912)

universal organizational science or tectology [267].

Bogdanov was the pseudonym of Alexandr Malinovsky.

Since the 1950s, the concept of system of systems has been

used “to describe systems that are composed of independent

constituent systems, which act jointly towards a common

goal through the synergism between them” [268].

Bertalanffy’s theory started from biology [71], which can

be seen as a system theory of life. The International Society

6

for the Systems Sciences (ISSS, earlier until 1988 Society for

General Systems Research) was founded in 1954 “to

encourage the development of theoretical systems which are

applicable to more than one of the traditional departments of

knowledge”. The International Council on Systems

Engineering (ICOSE) was founded in 1990. There are good

specific vocabularies on systems engineering, for example

[269]. In addition, the general standards dictionary is very

useful [270].

Bionics is the science of systems which have some

function copied from nature, or which represent

characteristics of natural systems or their analogs [96]. Many

other synonymous terms are used as well, for example

biomimetics.

Most fundamental limits of nature were discovered

between 1850 and 1950 [271]. Cybernetics by Wiener (1948)

combines the results of communication and control [83], [4].

Cybernetics has been followed by second and higher order

cybernetics, which is using positive feedback in addition to

negative feedback and is useful for self-organizing systems

[272], [85]. The origin of adaptive systems is in the work of

Darwin (1859) [81], but he used the term for self-

organization. In biology, self-organization is called

morphogenesis that was described by Thompson (1917) and

Turing (1952) [71], [80]. Morphogenesis can be described by

using second-order cybernetics and positive feedback so that

the system can diminish or grow when necessary [272], [41],

[85]. Emergence appears in morphogenesis when new forms

are created when moving to a higher hierarchy level.

Emergence has been studied since the work of Broad (1923)

[4]. Early work on self-organization was also presented in a

paper by Ashby (1947) [84]. He developed the first adaptive

system called homeostat as a simulation of the brain in 1948

[273].

The study of complex systems is closely related to

bionics [90], [91]. Human brain is the most complex system

that humans are aware of and therefore a good model for

engineering. There is yet no single complexity theory but

there are a number of theories, for example nonlinear

dynamical system theory, chaos theory, and catastrophe

theory and several computer models, for example cellular

automata, neural networks, and genetic algorithms.

The attempts to simulate the brain since the 1980’s are

summarized in [274], [275]. Reference [101] presents a

modern summary of theories of cognition. There are three

main approaches, including symbolic approach,

connectionism, and dynamicism. By far the dominant

paradigm is the symbolic approach where the mind is

assumed to be software in a computer. In connectionism, the

mind is assumed to consist of large networks of nodes. In

dynamicism, the mind is compared to continuously coupled,

nonlinear dynamical systems, including feedback loops. The

first cognitive architecture was the General

Problem Solver (GPS) by Newell and Simon (1976). The

most successful and widely applied cognitive architecture is

the Adaptive Control of Thought - Rational (ACT-R)

architecture by Anderson (1976), which relies on symbolic

representations and incorporates connectionist-line

mechanisms. The author of [101] ignores similar work done

in robotics [25], [11], [26]. Some of this work is directly

developing computational models of the mind. Thus, we can

see two parallel and independent tracks of research, one in

neuroscience [101] and the other one in robotics [25], [11],

[12], [26].

The Semantic Pointer Architecture Unified Network

(Spaun) by Eliasmith (2012) is the best simulation of human

brain [101]. This artificial brain includes 2.5 million neurons

(0.003% of human brain) and works in a similar way. Spaun

can recognize numbers 0-10 and solve simple tasks. One

modern approach to simulate the human brain is

neuromorphic chips [276], [277], [278], [279]. The term

neuromorphic was established in [93], [94]. The

implementation approach is digital: firing neurons send

spikes of electrical impulses that each corresponds to 40 bits.

The timing of the spikes conveys important information.

Neurosynaptic chips are event-driven and operate only when

they are needed, resulting in cooler operating environment

and lower energy use [280]. Some of the newest brain

simulations are summarized in [278].

There have been attempts to simulate the human brain

with a supercomputer. One second of 1% of human brain

functionality was simulated in 40 minutes, which implies

that there was over five orders of magnitude difference for

real time simulation of the whole brain [281]. Also in the use

of power (in W), there was over five orders of magnitude

difference since the power consumption of the

supercomputer was 12.66 MW, but the power consumption

of the human brain is only 14.6 W [97]. The reason for this

kind of large difference is a mismatch between the

computing task and the computing platform. The human

brain is not based on floating point multiplications. It has

been estimated that the number of synapses in the brain is 2.4

× 1014, the average firing rate of 1 ms spikes is 5 Hz, and

each spike corresponds to 50 floating-point operations [97].

Thus, the human brain has a computing speed equivalent to

6 × 1016 floating-point operations per second (FLOPS), and

the energy consumption per operation is 0.24 fJ, which is five

to six orders of magnitude lower than that of the most energy

efficient digital signal processor. On the average, only 3% of

the neurons are used at the same time [282]. Some authors

have proposed massively parallel architectures that would

have brain-like speeds [283]. A new paradigm for computer

architectures is based on memcomputing, inspired by the

brain [284].

Deep Blue, a chess-playing computer which won against

the human champion in 1997, is a milestone in smart

computing technology [285]. Deep Blue was followed by

Watson which in 2011 won against two champions of the US

TV quiz show with question-answering (QA) technology.

AlphaGo, in turn, was able to win in 2016 against one of the

best Go players in the world. A more advanced version is

AlphaGo Zero that can improve itself as a player when only

the rules of the game are given [286].

7

B. DECISION THEORY

Decision theory is a theory of choice, and its origins are in

the 1600’s [106], [108], [109]. Decision theory is concerned

with the choices of an individual decision maker. A subfield

of decision theory is operations research (1936) where

operations originally refer to military operations. Situation

awareness is an essential part of operations research to

support human decision-making in a dynamic environment

[57]. The term has been used since the World War I.

Situation awareness includes the perception of the elements

in the environment in time and space, comprehension of their

meaning, and the projection of their status to the near future.

The starting point of multiobjective optimization (MOO)

was Kuhn and Tucker’s paper (1951) about vector

optimization [106]. Modern multiple-criteria decision-

making (MCDM) theory started officially from the

conference Multiple Criteria Decision Making organized in

1972 [8].

The MCDM problems typically lack a unique solution.

Hence, the solution has to be determined based on subjective

preferences of the decision maker, that is, the decision

makers select one of reasonable alternatives that they prefer

[62]. The reasonable alternatives can be defined more

precisely by using the concept of Pareto efficiency [62],

[114]. Pareto optimal solutions (1906) are those solutions

where no improvement in any objective can be made without

worsening some other objective. The set of reasonable

alternatives is typically identified using MOO methods,

including scalarization algorithms (e.g., weighted sum or

weighted product algorithm). The function used in

scalarization is sometimes called the utility function and its

output is called the utility [234]. Alternatively, the set of

reasonable alternatives can be found using heuristic

methods, metaheuristic algorithms (e.g., evolutionary

algorithms, genetic algorithms, swarm intelligence, neural

networks), and other optimization algorithms (fuzzy logic,

game theory). Heuristic approaches differ from MOO since

they are not guaranteed to find the optimal solution but they

often find an approximate solution which is good enough.

The MOO methods are thoroughly reviewed in [8]. The

general problems in optimization include the large size of the

search space as to forbid an exhaustive search for the

optimum, modeling of a complicated problem, changes over

time requiring an entire series of solutions, and constraints in

the form of fundamental and practical limits [163].

Game theory by von Neumann and Morgenstern (1947)

[8], [106] is concerned with interactions of decision makers

whose decisions affect each other. In game theory, decision

makers, simply referred to as players, are assumed to be

rational, which implies that they know each other’s strategy

and each of them tries to maximize their own utility. The

game will finally lead to the Nash equilibrium (1950); to a

situation where no player can gain anything by unilaterally

changing his own strategy. In general, only if all the players

are cooperating, the system may converge to the Pareto

optimal equilibrium: the performance of no player can be

improved without decreasing the performance of some other

player. In practical situations, the players typically have

incomplete information. The problem of incomplete

information was solved by Harsanyi (1967) [106]. He

assumed that the information available can be modelled as a

Bayesian probability distribution on variables of interest.

Thus, the game with incomplete information can be

transformed into an equivalent Bayesian game with complete

information.

Game-theoretic considerations provide useful insight

into decision problems where a decision maker faces

multiple, possibly conflicting, criteria. The main idea is quite

simple: if all players cooperate, the result is the same as if a

single decision maker acts as a decision-maker for an

MCDM problem [62], [287], [9], [110].

Decision theory has produced such widely used

principles as linear programming or optimization by

Dantzig, Kantororovich, and Koopmans (1947), nonlinear

programming by Kuhn and Tucker (1951), dynamic

programming or multistage optimization by Bellman (1950),

and statistical decision theory by Wald (1950) [106]. Graph

theory by Euler (1736) and König (1936) and fuzzy sets and

fuzzy logic by Zadeh (1965) are also products of decision

theory.

C. CONTROL THEORY

Control theory is a theory of automata, based on the concept

of feedback although some control systems are open-loop

systems [10]. Control may be centralized, decentralized, or

distributed. In a decentralized system, the local control units

are autonomous [288] and compete with each other, but in

distributed systems, they cooperate at least with their

neighbors at the same level in the hierarchy [29], [64].

Modern systems based on remote control are called

networked control systems (NCSs), which now include

haptic information, i.e., both kinesthetic (sense of

movement) and tactile (sense of touch) information [28],

[244]. The NCS is a general concept that includes both

control over network and control of a network. Robotics is

heavily based on control theory. A robot is a machine

capable of carrying out a complex series of actions

automatically [122].

Feedback has been used since the antique [120]. The

feedback was applied first in a water clock by Ktesibios in

the third century BCE. In the modern period, the first to use

feedback was Dreppel who invented the thermostat early in

the 1600s [6]. Feedback was also included in Watt’s steam

engine (1769) and later analyzed by Maxwell (1868) but the

analysis was forgotten until Wiener (1948) [128], [121].

Lyapunov presented a theory for stability of nonlinear

dynamic systems in 1892, but this result was also forgotten

until about 1960 [289]. The now common proportional-

integral-derive (PID) controller was developed by Minorsky

(1922) by studying the steering of a ship [10].

The negative-feedback amplifier was invented by Black

(1927), but his patent application was accepted only in 1937

since still at that time the idea looked controversial [121].

Feedback was used to reduce distortion. The starting point of

modern control theory and intelligent systems is Nyquist’s

8

stability analysis of the feedback [53] after which the

generality of this old concept was finally realized. Nyquist

was Black’s assistant. Feedback can help finding a solution

iteratively when it is not possible to find it directly [290],

[111]. In the iterative approach, a solution is kept in a

memory and updated in each iteration. Feedback is used for

two general purposes, namely for finding a solution in an

unknown noisy environment and for tracking changes in the

environment. Feedback must be slow enough so that it can

reduce the effects of noise, but fast enough so that it can track

the changes in the environment. A delay within the control

loop has detrimental effects on the stability and convergence

of the loop [291], [145]. Delays slow down the loop, and

such delays should be avoided as much as possible. For

example, for machines on or near the Moon, the unavoidable

delays are typically 3 s.

Feedback algorithms are often called closed-loop

algorithms. In some cases, it may be necessary to use open-

loop, feedforward, or block algorithms that try to find the

solution either directly in one shot or iteratively, but without

using the sensing output from the environment [262], [133],

[10]. Such systems are working blindly [6], using for

example a predefined time such as in a washing machine or

in traffic control [10]. The advantages and disadvantages of

open-loop control are summarized in [10]. Open-loop

control is simpler than closed-loop control, there is no

stability problem, and no need to measure the process output.

Moreover, open-loop control has the benefit of being faster

than the closed-loop control, which is important in many

applications since closed-loop algorithms may be very slow,

especially when there are many variables to be controlled.

However, for open-loop control, the convergence time may

not be easy to predict and depends on the number of degrees

of freedom or the number of variables [133]. In addition,

disturbances may cause errors and frequent calibration may

be needed.

In communications, open-loop control is sometimes used

in synchronization and channel estimation [292] and

transmitter power control where the reciprocity of the

channel is used [293]. In transmitter power control (TPC),

the closed-loop control may be impractical if the delay in the

feedback channel is excessive. Open-loop control is used in

some biological systems such as human brain [294].

In many cases, the optimum cannot be found with a

feedback loop only, but it is found in two phases called

acquisition and tracking [295], [208] where open and closed-

loop control are combined. There may be local optima that

must be avoided. In acquisition, a rough estimate is first

found by using an open-loop algorithm based on exhaustive

grid search with a suitable resolution. An alternative is

random search [5]. Acquisition is often based on a

correlation process so that the tracking phase may start.

Correlation is an optimal approach if the additive noise is

white and Gaussian. Tracking is usually iterative and based

on a feedback loop and therefore it may converge to the

global optimum only when it starts close enough.

In communications, the signal-to-noise ratio per symbol

is usually rather small, and averaging of channel parameters

must be done over several symbols [296]. The same rule is

valid for open-loop systems. Usually, the feedback works

iteratively, but in some cases, a direct or one-shot solution

may be used in the feedback, updated every now and then

[111].

Adaptive systems became popular at the end of the

1950’s. Adaptive control was first presented in [297] by

borrowing the term from biology. Adaptive control systems

are systems that monitor their own performance and adjust

some of their variables in the direction of better performance.

The environment may be slowly changing. The workhorse in

adaptive systems is the least-mean square (LMS) algorithm

[263], [208], [134], which was devised by Widrow and Hoff

in 1959 but whose original version was presented by von

Mises and Pollaczek-Geiringer (1929) and Robbins and

Monro in 1951 [132], [134], [238]. An early review paper is

[298], whereas [299] includes a bibliography of 49 papers.

Saridis derived in 1988 optimal control by using the entropy

concept [5]. He showed that the entropy is minimized by

using a three-level hierarchy if the process has widely

different time resolutions or scales, i.e., having at least an

order of magnitude difference, which makes the hierarchical

control possible. A similar conclusion can be drawn for

different spatial and frequency resolutions. The history of

self-organizing control was started by Mesarovic [63] and

further developed by Saridis [300].

The first learning control systems were summarized in

[301], [302], [303]. According to [302], the problem of

unsupervised learning was defined in machine learning and

pattern recognition by Abramson (1963). The first

unsupervised learning control systems were described in

[304], [302], [305]. One of the first books on adaptive and

learning control systems was [132] whose original version

was published in Russian in 1968. Tsypkin used the term

self-learning to describe unsupervised learning. According to

him, the term “learning without a teacher,” a term used at

that time for unsupervised learning, is a misnomer since in

self-learning there is really a teacher that has defined the

methodology for learning [305]. Intelligent control was

originally proposed in [306], but a clear definition was given

in [46]. The same definition was later in 1993 adopted by the

IEEE Control Systems Society for intelligent control [307].

The term autonomous control became popular in the

1980’s [135], [128], [12]. Originally, the term intelligent

control was used instead of autonomous control [126]. The

authors of [135] have the opinion that autonomy is the real

objective and intelligent controllers are one way to achieve

it. The first mobile robots had already a form of autonomy.

The term “robot” was first used to denote an automaton

in 1917 by J. Čapek [6]. The actual history of robotics started

from a robotic arm by Pollard (1938) [122]. The first tests

with remote control (at that time known as teleoperation)

were made by Goertz who started his work on an

electronically controlled telemanipulator in about 1945 and

published it with Thompson in 1954 [33], [35], [241]. Tesla

9

demonstrated a radio-controlled miniature boat already in

1898. In 1966, Ferrell published contact force feedback that

was the first paper to consider delays in control loops. It was

the first rudimentary haptic kinesthetic feedback system

where a delay as small as 100 ms was shown to destabilize

the teleoperator. One of the first NCSs was the Control Area

Network (CAN) developed in 1983-1986 [34]. Anderson and

Spong (1989) published a paper solving for the first time the

stability problem caused by the feedback delay [35]. In some

industrial applications, a 1 ms control delay is a requirement

[31]. Remote control over the Internet was started by

Goldberg (1995) [147].

The reaction times of human senses are summarized in

[105]. The speed of electrochemical signals in human nerves

is in the order of 10-100 m/s. If the speed is 100 m/s, the

signal propagates 10 cm in 1 ms. Our ears have a reaction

time of 140-160 ms, including the delay of the brain, our eyes

have a reaction time of 180-200 ms, and the sense of touch

has a reaction time of 155 ms, Auditory stimulus takes 8-10

ms to reach the brain and visual stimulus takes 20-40 ms,

which makes the difference in reaction times. A bulk of the

reaction time is used by the brain to make a decision. The

shortest reaction time of a sprinter is 100 ms, otherwise, it is

a false start according to the International Association of

Athletics Federations (IAAF) [308].

According to [146], the first review paper on tactile

interaction was published in 1984. The number of papers

started to grow in 1991. McCauley and Sharkey (1992)

published the first paper on cybersickness caused by the

closed-loop delay in the control loop that carries information

on the sensations of sight and touch [309], [151], [31]. The

delay should not be much larger than 1 ms. A more detailed

up to date discussion is in [153], [310]. The requirement

depends on the task at hand. The most demanding tasks in

this respect are dragging on a touch screen (maximum delay

of 1 ms) and inking or line drawing using a stylus (maximum

delay of 7 ms). Mouse control allows 60 ms delay [311]. The

sense of touch can differentiate between two stimuli just 5

ms apart [149], [151]. Our ear can resolve clicks separated

by 0.01 ms, while the eye requires 25 ms [312]. Touch is

highly sensitive to vibrations up to 1 kHz with the peak

sensitivity at about 250 Hz. In 2001, Elhajj et al. published a

review paper on haptic feedback over the Internet,

complementary to the conventional audiovisual feedback

[35], [241].

The development in robotics has continued from a

robotic arm towards mobile robots, bipedal walking robots,

and finally to cooperating robots [122]. An important branch

of robotics is microrobotics, which is leading to

nanorobotics. The names describe the size of the robots in

the micrometer and nanometer range, respectively.

Heinlein first proposed in 1942 a primitive telepresence

master-slave manipulator system. The term telepresence was

coined in a 1980 article by Minsky [21], who outlined his

vision for an adapted version of the older concept of

teleoperation that focused on giving a remote participant a

feeling of actually being present at a different location. One

of the first prototypes on virtual reality was developed by

Heilig (1962).

The results of control theory and computer science are

nicely combined in autonomous robots, which are agents at

the same time [25], [11], [12], [26]. Early mobile robots were

the two tortoises built by Walter in 1948-1949 [12], [313].

They were also the first autonomous robots. One of the first

robot architectures in the 1960’s was Shakey by Nilsson

(1969). In the beginning, only one hierarchy level was used,

but such robots were slow and ponderous. The most common

mixed architecture is based on the layer hierarchy. Brooks’

reactive subsumption architecture (1986) was the first

significant improvement where the layers were connected to

the sensors and actuators directly as in [136], leading later to

Arkin’s autonomous robot architecture (1990). In [11], [12]

several architectures are summarized to express a

computational model of intelligence. The authors of [11] are

using a model called Real-Time Control System (RCS),

originally developed by Barbera (1979). In modern proactive

architectures originated by Firby (1989), the lowest layer is

responsible for the movement. The next layer is responsible

for choosing the current movement, and the uppermost layer

is responsible for achieving long-term goals within resource

constraints using planning. Newest developments are

summarized in [190], [192], [61].

One of the first outdoor navigation systems was the

autonomous car reported by Tsugawa in 1979 [155], [158].

The car could drive autonomously at 30 km/h. It used a pair

of stereo cameras mounted vertically to detect expected

obstacles. The navigation relied mostly on obstacle

avoidance. Such self-driving or driverless cars are a form of

autonomous ground vehicles (AGVs). Similar principles

have been later developed for unmanned aerial vehicles

(UAVs) and autonomous underwater vehicles (AUVs).

Navigation can be map-based or mapless. Unmanned surface

vehicles (USVs) or autonomous surface vehicles (ASVs)

such as autonomous ships operate on the surface of the water

without a crew [159]. A large proliferation of USV projects

appeared in the 1990’s.

D. COMPUTER SCIENCE

Computer science includes two areas that are of interest to

us, namely artificial intelligence and computational

complexity analysis. The goal of computational complexity

analysis is to find efficient algorithms in terms of time and

space or the size of the memory [13]. The computational

complexity analysis started from the work of Hartmanis and

Stearns (1965) and was later complemented by Cook, Levin,

and Karp [171].

Artificial intelligence is a science of intelligent agents

[6]. Agents sense their environment and perform actions.

Machine intelligence is divided into artificial and

computational intelligence [314]. The artificial intelligence

refers to conventional hard computing methods using binary

logic, whereas computational intelligence includes such soft

computing methods as neural networks, evolutionary

computing (e.g. genetic algorithms), fuzzy computing, and

10

biologically inspired algorithms (e.g. swarm intelligence)

[170]. Some of the methods may be classified to belong to

decision theory but they are applied extensively in computer

science.

Augmented intelligence, also called intelligence

amplification, is complementary to artificial intelligence

referring to the use of computer science in augmenting

human intelligence. Ambient intelligence (AmI) brings

intelligence to our everyday environments and makes those

environments sensitive to us [178], [179]. The term was

coined by the European Commission in 2001. Ambient

intelligence research builds upon advances in sensors and

sensor networks, context-aware computing, pervasive or

ubiquitous computing, and artificial intelligence. Context

awareness is defined as the use of context to provide task-

relevant information or services to a user [180], [240], [181].

The term was introduced for the first time by Schilit in 1995

[240]. Cyber-physical systems (CPSs) are integrations of

computation and physical processes, combining computing,

control, and communications [195], [23], [196]. The

National Science Foundation (NSF) has defined CPS as

systems “where physical and software components are

deeply intertwined, each operating on different spatial and

temporal scales, exhibiting multiple and distinct behavioral

modalities, and interacting with each other in a myriad of

ways that change with context” [196]. CPS covers NCS

whose origin is in control theory [28]. The predecessors of

CPS were embedded systems [315], [316].

The starting point of computer science was the digital

computer (1946) and the supercomputer (1958), which led to

the concepts of artificial intelligence (1956), machine

learning (1959), and computational intelligence (1994)

[106], [122], [314], [162]. Neural networks were introduced

even earlier by McCullough and Pitts (1943) and the first

perceptron rule was developed by Rosenblatt (1960) [317],

[318], [319]. An important step was the multilayer neural

network, whose various versions were developed since 1965,

and later the term deep learning was used for large multilayer

networks since 1986. The pioneers of artificial intelligence

were Newell, Simon, McCarthy, Minsky, and Samuel.

McCarthy proposed the term artificial intelligence and later

Samuel proposed the term machine learning. The first

practical AI systems were expert systems by Feigenbaum

(1975) [122].

The terms cloud and edge computing were first used in

about 1996. Edge computing refers to computing in devices.

Fog computing is an intermediate between cloud and edge

computing, so that the cloud is in the local area network near

the edge of the network.

The origins of big data are in pattern recognition which

has a long history in statistics [320], [321]. The first papers

in the IEEE literature were published in 1954 [322].

Knowledge discovery and data mining started in about 1990.

The term big data was first suggested by Mashey (1998) after

which it became popular [323]. It has applications in

artificial intelligence. The appearance of Internet of Things

(IoT, 1999) has made the available data especially large.

Software agents were developed by Hewitt (1977). This

lead to multiagent systems (MAS) that were earlier called

distributed AI in about 1980 [187]. Multiagent systems pose

the problem of distributed learning. Mobile agents (1995) are

a more recent development [324]. They were first called

itinerant agents. More recently, autonomic computing (2001)

[50] and cyber-physical systems (2006) [23] were

developed. The first conference on distributed computing

was organized in 1982, but the history is much longer and

related to the first computer networks. According to [193],

the origin of autonomic computing was a project called

Situational Awareness System (SAS) funded by the Defense

Advanced Research Projects Agency (DARPA) in 1997. The

project’s aim was to create personal communication and

location devices for up to 10,000 soldiers on the battlefield.

Decentralized multihop ad hoc routing was used in a difficult

environment with the goal to keep round-trip latency below

200 ms. Thus, autonomic computing is a form of self-

organizing computing and its history is in distributed

computing.

E. COMMUNICATION THEORY

Communication theory is a theory of data transmission

through a network of links [15], [16]. Usually, also redundant

control information is needed to recover the transmitted data.

Programmable networks include active networks (1996)

and software-defined networks (1998) [214]. A software-

defined network (SDN) is a programmable network that

separates the control plane from the data plane [214], [217],

[218]. The network intelligence is logically centralized in

software-based controllers and the network switches become

simple forwarding devices. The SDNs represent centralized

systems.

A self-organizing network (SON) is an autonomous

network organized without any external control unit [224],

[51]. A self-organizing network “will create its own

connections, topology, transmission schedules, and routing

patterns in a distributed manner” [222]. Routing is an

example of self-organization. The individual nodes interact

directly with each other in a distributed fashion. The idea of

self-organization in communications may be traced back to

packet switching (1964) and dynamic channel assignment

(DCA) (1971) [253], [199]. Packet switching eventually led

to the invention of Internet Protocol by Cerf and Kahn

(1974), which is the basis of the Internet. An ad hoc network

is a collection of, possibly mobile, communication nodes that

wish to communicate but have no fixed infrastructure

available [223]. Ad hoc networks are therefore always

SONs. They have also been called packet radio and multihop

networks. Self-organization can include centralized control

if the control unit is within the network. Usually, SONs are

implemented using distributed control [222], [224].

Ideas from different disciplines are now merged for

example in Tactile Internet [30], [31], which has its roots in

teleoperation [33], networked control systems [28], and

haptic communications [35]. In 2010, Lenay mentioned the

term Tactile Internet for the first time in the scientific

11

literature, and the concept was introduced in more detail in

[31], [32]. Tactile Internet is a version of NCS with the haptic

feedback, both originally developed in control theory [28].

Some authors have proposed a more general term Haptic

Internet [244]. Time sensitive networking (TSN) is a set of

standards being developed since 2012 to support time

synchronized low latency traffic [24]. Internet of Skills

combines the results of communications, robotics, artificial

intelligence, and Tactile Internet [325]. Skills are capabilities

or behaviors that a human being or an agent may have [25],

[26]. Examples include remote surgery, education, and

reparation.

Autonomic computing resulted in the term autonomic

networks whose special case is cognitive networks [51],

[232], [236]. Autonomic networks are essentially self-

organizing networks. Regarding bionics, bio-inspired

networking and signal processing [265], [266] have been

studied for some time.

Much of the theoretical basis of communications is in

graph theory by Euler (1736), electromagnetic field theory

by Maxwell (1861), queuing theory by Erlang (1909), time-

frequency analysis by Gabor (1946), information theory by

Shannon (1948), and statistical decision theory by Wald

(1950). Before the statistical decision theory, optimization

was done by using the signal-to-noise ratio without any

statistical information [198]. The optimal receiver was the

matched filter which was invented many times. One of the

earliest inventions was made by W. W. Hansen of Stanford

University in 1941 although usually North (1943) is seen as

the original inventor [208], [198]. Matched filtering

corresponds to correlation. More generally, the optimal

receiver is the maximum a posteriori probability (MAP)

receiver by Woodward and Davies (1952). The graph theory

and statistics were combined in random graphs, developed

by Erdős and Rényi (1959) [106]. We concentrate on

intelligent aspects used in communications, including

hierarchy and feedback.

One of the common biggest efforts of computer and

communication scientists was the Open Systems

Interconnection (OSI) model that was finalized in 1984

[326], [15], [16]. It is a layer hierarchy that includes seven

layers, that is, the physical, data link, network, transport,

presentation, session, and application layers. The Internet is

using a modified version of this model, now called

Transmission Control Protocol/Internet Protocol (TCP/IP)

model where the most important protocols are in the

transport and network layers, respectively. The presentation

and session layers are excluded from the TCP/IP model. One

trend is to simplify the protocols for specific purposes, for

example [327]. The name of the protocol data unit (PDU)

depends on the layer [15], [16]. In the physical layer, the

PDU is a bit or symbol, in the data link layer, it is a frame,

and in the network layer, it is a packet. A data packet is

similar to a postal packet in the sense that it carries the

address of the destination with it. Each packet is routed in the

network layer from the source to the destination through the

network, and the packets are collected and put in the right

order in the transport layer. In the TCP/IP model, the PDU

in the network layer is a datagram, in the transport layer, it

is a segment, and in the application layer, it is a message or

bit. Hierarchy has been used in various other forms in

communication networks, including framing [15], routing by

Kleinrock (1977) [248], and network synchronization by

Lindsey (1980) [246], [247].

The use of feedback in communications started from the

phase-locked loop (PLL) by Appleton (1923) [328],

automatic gain control (AGC) by Wheeler (1925) [329],

automatic frequency control (AFC) by Travis (1935) [330],

and delay-locked loop (DLL) by Guanella (1938) [331]. One

of the first adaptive receivers was the Rake receiver by Price

and Green (1958) [208]. It is a form of adaptive matched

filter and an adaptive multipath diversity combiner, but the

authors did not use the term “adaptive” since it was not

commonly used in engineering at that time. Instead, the term

“automatic” was used. The adaptive equalizer was developed

by Lucky (1965, 1966) to combat intersymbol interference

(ISI) [332], [264]. Applebaum (1966) and Widrow (1967)

were the first to write about adaptive antenna beamforming

[133], [134]. Control theory is also widely used in adaptive

transmission, for example in TPC and adaptive modulation

and coding (AMC) [333], [202]. TPC research started from

the work of Hayes (1968), which was later complemented by

adaptive bit rate control by Cavers (1972). The ideas were

combined by Hentinen (1974). Adaptive automatic repeat

request (ARQ) schemes started from the work of

Mandelbaum (1972) [334], AMC in fading channels from

the work of Vucetic (1991) and Alamouti and Kallel (1994)

[333]. Such adaptive transmitters usually need a feedback

channel from the receiver to the transmitter.

Most of the feedback mechanisms belong to the physical

or data link layer. Feedback is also used in flow control and

congestion control in the transport layer [16]. Flow control

means sender and receiver speed matching because of finite

buffers. Congestion control is needed because of the finite

capacity of the network. Feedback can also provide

information on throughput, delay, jitter, reliability, and other

network properties in the transport layer [15]. The properties

form the state of the network.

In wireless communications, spectrum management is

used to avoid interference within a system and between

systems [335], [256]. Spectrum allocation has traditionally

been static in the form of spectrum regulation [336], [337].

After the introduction of cognitive radios by Mitola (1999)

[58], the idea of dynamic spectrum allocation (DSA)

appeared to manage the use of spectrum within a system

[254], [256]. An earlier similar term is radio resource

management (RRM) used for resource management between

different systems [335]. Usually, the radio resource of

interest is spectrum. A precursor of DSA was dynamic

channel assignment by Cox and Reudink (1971) [253]. DCA

and RRM were precursors of the cognitive radio concept.

Applications of artificial intelligence in communications are

introduced in [239], [231]. In addition, the use of agent based

12

optimization and big data are considered communications in

[338], [339].

Network traffic monitors and network analyzers have a

long history since about 1980. The general term network

awareness was proposed in [340]. It is quite similar to

situation awareness in operations research [57] and context

awareness in computer science [181]. Network sensing or

monitoring is the process of collecting information about

network performance. The term network monitor refers to an

entity in charge of the network-sensing tasks in a computer

or network. Existing network-aware systems monitor such

variables as available throughput, latency, packet loss rate,

and the load for different computers in the network.

III. RECOGNIZED CONNECTIONS BETWEEN DISCIPLINES

The connections that we have observed between the five

disciplines are presented in this section. The basic concept is

the control loop (Fig. 1b); in all the disciplines, the plant is

controlled to achieve or keep some state of the system or to

achieve some behavior. When a single decision block is not

sufficient, the blocks must be hierarchical and distributed.

Generally, hierarchy is used to limit the complexity of a

system, especially when there are different spatial, time, and

frequency resolutions [63]. A decision block has usually a

limited decision capability, and therefore the overall goal of

the system is divided into more manageable subgoals. We

focus on control hierarchy, that is, how the decision-making

is organized and how control signals flow in the hierarchy.

Hierarchies are important also for conceptual analysis to

show explicitly the relationships between different concepts

and the different terminology in different disciplines.

Systems are organized in a hierarchy according to the

complexity of the decision block. The classifications

emphasize differences rather than excluding the possibilities

of a system belonging to more than one class [63]. Many of

the ideas were first developed in control theory, but they are

useful in all disciplines. Communication networks have a

spatial extent by definition since the users are spatially

distributed, and the networks enable distributed control and

computing. We include also basic resources in the analysis.

The fundamental limits will lead us to multiple-criteria

decision-making.

A. CONTROL LOOP

Many of the most advanced systems in Fig. 2 are based on

the feedback concept. A general definition of feedback is

“the transmission of information about the actual

performance of any machine (in the general sense) to an

earlier stage in order to modify its operation” [4]. More

narrowly, feedback control refers to “an operation that, in the

presence of disturbances, tends to reduce the difference

between the output of a system and some reference input and

does so on the basis of this difference” [10]. We prefer the

more general definition from [4] since some feedback

systems based on unsupervised learning do not need a

reference signal, but their goal is to optimize some

performance criterion, possibly observing certain

constraints. In negative feedback, the modification is such as

to reduce the difference between actual and desired

performance; positive feedback in general induces instability

by reinforcing a change in performance. In a complex control

system, there may be a positive-feedback inner loop. Such a

loop is usually stabilized by the outer loop. Positive feedback

is used especially in some learning and self-organizing

systems.

The general feedback control loop consists of sense,

decide, act, and plant blocks [63], [124], [10], [6], see Fig.

1b and Fig. 5 for an alternative form. These four blocks form

the system being studied that can be called the control

system. The plant, i.e., the process, is the device or operation

being controlled. The control is realized with the sense,

decide, and act blocks. The plant itself may include control

loops that have been left outside the scope of the control loop

being studied. The interfaces between the blocks must be

defined by the observer. We focus on the decision block that

can be called control unit or controller. The plant is

controlled by performing actions according to the decisions

made by the decision block. The state including the

properties or performance of the plant is monitored with

sensors; this provides performance feedback [63], [4]. The

decision block may use external information in the form of a

set-point value or reference signal. The decision block is

goal-seeking, i.e., its purpose is to solve some possibly

constrained optimization problem so that the control system

obtains the maximum performance.

FIGURE 5. The control system in Fig. 1b in an alternative form. D refers to the decision block and P refers to the plant or process.

The sensor measures the state of the plant. The state of

the system is the total of all the measures of the performance

at a given time [7]. Usually, the state is expressed by using

certain KPIs such as throughput, delay, and reliability [65].

The desirable states are goals that are in general mutually

conflicting. Sensing is part of perception that is a process by

which sensory input is transformed into structured

information or knowledge, useful for reasoning, about the

environment [11]. In communications, the sensor is often

called the monitor [340]. The decision block essentially

makes choices of actions based on the sensed data. The

actuator executes the selected action. Acting may be also

called executing [26]. The decision block may include a

memory to which it collects the data and an input for a set-

point value or a reference signal. In self-organizing systems,

the actions change the structure of the system in an

autonomous way.

The decision block is a key element in the feedback loop.

In general, the block is deterministic or partially random. A

deterministic algorithm implies that when the input data are

the same, the output data are always the same. Because of

the feedback, the input data are changing.

B. HIERARCHY CONCEPT

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An important general concept developed in control theory is

hierarchy. The human brain has a hierarchical structure

according to some theories [99]. A classification of

hierarchies is presented in Fig. 6 [63]. The terminology is

different in different disciplines. There are three main

hierarchies, including nested hierarchy (also called stratified

hierarchy), layer hierarchy (also called multilayer

hierarchy), and dominance hierarchy (also called

multiechelon hierarchy). In robotics, the layers are

sometimes called tiers [26]. The dominance hierarchy may

be also called organizational hierarchy or more loosely tree

hierarchy. We will use the descriptive terms nested, layer,

and dominance hierarchy. The term level is reserved as a

generic term referring to a hierarchy level. Mesarovic calls

the levels in nested, layer, and dominance hierarchies as

strata, layers, and echelons, respectively. Any multilevel

system may in general be described by using more than one

of these hierarchies [124]. When considering control, these

three hierarchies can be used to organize control decisions

and the flow of sensing and control signals.

FIGURE 6. Three common hierarchies.

The nested hierarchy is often used as a description or

abstraction hierarchy [63]. Most natural and human-made

systems can be described using this abstraction hierarchy and

modularity. In the nested hierarchy, the lower level systems

are inside the upper level systems, whereas in other

hierarchies, lower level systems are below and not inside the

upper level systems. The layer hierarchy by Lefkowitz and

dominance hierarchy by Mesarovic are the two classical

decision hierarchies in control theory [127]. In the layer

hierarchy, each level has a different time resolution but

shares a single goal. In the dominance hierarchy, each level

has a different time, frequency, and spatial resolution, and

there can be many conflicting goals. The dominance

hierarchy is especially useful for centralized control when

the plant is spatially distributed. The layer hierarchy is a

special case of the dominance hierarchy [63].

Information exchange between spatially separated

decision units can be done by using a shared memory or

database, message passing, or their mixture [167]. The

shared memory architecture is often called the blackboard

architecture.

In the diagrams (Fig. 6), the decision blocks interact

directly only with the nearest hierarchy level. This is only

superficial since any of the decision blocks may transmit

information to any other level. For example, Meystel’s

multiresolutional architecture is a special case of the

dominance hierarchy where each of the levels communicates

directly with the process but with a different resolution [136].

A system can be modeled at four abstraction levels,

namely, functional, behavioral, structural, and physical

levels (Fig. 7). We have combined the terminology used in

different disciplines [7], [20], [341], [342] to give a unified

picture. The functional level describes the input-output

relationship of the system. It corresponds to the systems

view. The model at the functional level may be called a

mathematical or black-box model having no internal

behavior or structure that would represent the reality [343].

At the behavioral level, the behavior is defined as a set of

successively attained states of the system where the state

includes all the properties of a system at a given time [7]. In

our case, the properties are measured with performance

metrics. Some authors consider functional and behavioral

levels to form one level. At the structural level, the structure

is the set of parts and their relationships. The structure is

“deeper” in the system and therefore more difficult to change

than the behavior. This is done in self-organizing systems

where organization is the same as structure. The structural

level is sometimes called architectural level. The model in

the behavioral and structural levels is called a theoretical

model, more detailed than the mathematical model [343].

The physical level of the system is located below the

structural level defining the physical parts of the system. It is

an analytical or reductionistic view to the system.

FIGURE 7. Typical abstraction levels of a system in different disciplines.

C. HIERARCHICAL AND DISTRIBUTED CONTROL

Hierarchical control was first extensively studied in [63].

Based on this work, the hierarchy in [126] has been so far the

standard approach in control theory, intelligent agents, and

robots [128], [25], [26] although the terminology may be

different. Control is often structured hierarchically because

the decision-making capability of a single control unit may

be limited, subsystems may be far from each other and have

limited communication with each other, there is a cost, delay,

or distortion in transmitting information, and subsystems

make decisions autonomously [127].

Usually, in a layer or dominance hierarchy (Fig. 6), three

functional levels are used: controller, coordinator, and

manager or organizer (Fig. 8). The hierarchy is based on the

use of different time and spatial resolutions, and it has also

strong analytical evidence based on the entropy concept [5].

However, many variations have been proposed with a

different number of levels, and with different names for the

levels as well; some examples are given below.

FIGURE 8. Hierarchical control in a layer and dominance hierarchy. Examples are given from robotics and communications.

The controller is a control system that has the lowest

complexity and the highest speed in the hierarchy and works

in real time with high accuracy. It has only a local view on

the system. It has no memory and it is using high time,

frequency, and spatial resolutions [249]. The coordinator is

a learning system that does not work in real time and it has a

short-term memory, which gives it a possibility to learn from

earlier experience. The manager or organizer is a self-

organizing system that has the highest decision complexity

and the lowest speed. It uses a long-term memory and low

spatial, time, and frequency resolution and has an overview

on the whole system. It can change the organization or the

structure of the system, thus the name organizer. In general,

the organizer must be capable of planning. Planning is a

14

reasoning process by which a system predicts the future and

selects the best course of action to achieve the goal [11].

The dominance hierarchy (Fig. 6) forms the basis of

centralized control in control theory. In communications

theory, the implementation can be based on an SDN [344].

Such a system works reliably but needs much control

information. The amount of control information is

minimized when the time, frequency, and spatial resolutions

at each hierarchy level are different. That is the case in most

practical situations. In robotics, the levels are sometimes

called behavioral, executive, and task-planning layer [26].

In the OSI model, the physical layer corresponds to the

controller (for example synchronization and adaptive

equalizers and estimators resemble control algorithms), the

data link layer corresponds to the coordinator (for example

medium access control is used to share the link resources

using a frame structure), and the network layer corresponds

to the organizer (for example routing). The time resolutions

can be mapped to each of the layers using the parameters in

the present fourth generation (4G) Third Generation

Partnership Project (3GPP) “Long Term Evolution –

Advanced” (LTE-Advanced) system [205]. The radio frame

of 10 ms is divided into ten subframes of 1 ms, each

including 14 orthogonal frequency division multiplexing

(OFDM) symbols having a subcarrier spacing of 15 kHz.

There are four essential time resolutions [205], [345]. These

are the sampling interval 32.5 ns, symbol interval 66.7 s,

subframe 1 ms, and mean user interarrival time, which

depends on the prevailing network congestion condition and

is typically assumed to be much higher than 1 ms.

The physical layer processes samples and symbols. The

maximum sampling interval used for example in filtering is

dependent on the inverse of the maximum bandwidth 20

MHz. The symbol interval used in modulation is the inverse

of subcarrier spacing and is large enough to minimize the

effects of intersymbol interference because of multipath

propagation. The data link layer operates with frames and

their subframes using the 1 ms resolution for, e.g.,

scheduling and link adaptation. The length of the subframe

corresponds to the coherence time of the channel with the

maximum terminal speed of 350 km/h at 2.1 GHz. Routing

belongs to the network layer. The functions of network layer,

such as admission control, are executed during the setup of

new user data flows within the time resolution that depends

on the observed mean user interarrival time. Typical

periodicity for some self-organizing decisions such as load

balancing is within 1-10 s. Thus, the different layers operate

with widely different time resolutions and bandwidths using

the idea of hierarchy in Fig. 8. In the quality of service (QoS)

requirements, the actual delays in the data link layer are

between 50 ms for real-time games and 300 ms for file

transfer.

In hierarchical routing, the three functional levels are

data plane, control plane, and management plane [249]. The

data plane (also called the user plane) performs packet

forwarding and implements functions such as queue

management and packet scheduling. The data plane is local

to an individual router and operates at the speed of packet

arrivals. A primary task of the control plane is to compute

the shortest routes between IP subnets. The control plane

operates at the time resolution of seconds without having a

complete view of the whole network. The management plane

stores and analyzes measurement data from the network and

generates the configuration state on the individual routers.

The management plane operates at the time resolution of

minutes or hours and has the spatial view of the whole

network.

In network function virtualization (NFV) [346], an

additional level, orchestration, is located above management.

Management creates and manages the infrastructure called

virtual network that consists of virtual resources: data links,

routers, memory capacity, and processing capacity.

Orchestration collects the virtual resources needed by an

end-to-end service from the virtual resources provided by the

manager.

Control systems can be classified according to the degree

of centralization into centralized and decentralized control

systems and their mixed forms, from which the most relevant

ones are distributed or clustered control systems [64] (Figs.

9 and 10). A similar classification can be presented for

computing systems [29]. The trade-off between edge and

cloud computing [347] is closely related to the degree of

centralization. In centralized control, a central control unit

controls all parts of the system using sensor information. In

decentralized control, many goals compete with each other,

and the local control units do not communicate with their

neighbors although they may sense their environment.

Hence, the local control units are autonomous [288].

Distributed control is a mixture of centralized and

decentralized control. This control system type should not be

mixed up with the “hybrid system” term in control theory

and computer science that refers to a combination of

continuous-time and discrete-time systems. In distributed

control with many goals, the local control units cooperate

with their neighbors by forming clusters, coalitions, teams,

swarms, or platoons, and the clusters may compete with each

other. Distributed control has the performance advantage of

centralized control while maintaining scalability, ease of

implementation, and robustness of decentralized control

[64].

FIGURE 9. Degree of centralization. a) Centralized, decentralized, and distributed control. b) Distributed control combined with dominance hierarchy using clusters of control units. FIGURE 10. Properties of systems with different degrees of centralization.

The degree of centralization can be combined with the

hierarchy concept. Fig. 9b [63] presents an example of

combining distributed control with dominance hierarchy.

Purely decentralized control schemes do not have enough

information to make fast decisions without an extensive

amount of trial-and-error iterations. In the centralized

approach, sensing information moves upwards in the

hierarchy and the control information moves downwards.

15

While a global decision maker at the highest hierarchy level

enables noniterative optimal decisions, the system may

become unreliable, require high signaling overhead, or lead

to large control delays. Another way to use the hierarchy

concept is to rely on distributed decision-making but allow

sensing information to be exchanged between the hierarchy

levels. An example of such a distributed approach is

presented in [348]. The raw control data obtained from the

lowest hierarchy with local network nodes is centrally

aggregated and distributed back to local nodes. The local

control units can then work autonomously based on a

combination of a locally sensed information and the optional

aggregated awareness of the overall situation. This may help

to overcome the aforementioned bottlenecks of purely

centralized or decentralized control approaches.

D. HIERARCHY OF SYSTEMS

The performance feedback concept in Fig. 5 and the

behavioral and structural levels in Fig. 7 form the basis of the

general hierarchy of systems (Fig. 11). We have formed this

hierarchy according to the increasing uncertainty and

sophistication of the decision block in Fig. 5. Each hierarchy

level includes conceptually the lower level as a special case.

When one moves upwards in the hierarchy, both the

complexity and energy consumption of the decision block

are increased. Any system should be implemented at the

lowest possible level to reduce power consumption as much

as possible, possibly by using decentralized or distributed

control to reduce communication between the different parts

of the system.

FIGURE 11. General hierarchy of systems in different disciplines according to the complexity of decisions. The structure changes only in self-organizing systems, otherwise the behavior changes, see Fig. 7.

The starting point for the hierarchy is [73], which was

originally developed mainly for natural systems. We have

also used the hierarchies in [63], [126], [20] and modified

them to human-made systems and used the newest

terminology. To support our views, we have also used many

other references [297], [301], [63], [303], [306], [126], [41]

to define the names of the hierarchy levels and their order.

Some other hierarchies have been presented in the literature,

for example in [227], but the hierarchy shown in Fig. 11 has

the longest history developed into this form already by the

year 1970 [73], [63] and still commonly used in control

theory [128], [5].

The lowest hierarchy level includes static systems, which

are fixed structures such as passive analog filters with no

decision capability. No energy is consumed for information

processing. Passive filters differ from active filters in that

they do not use any power supply. Thus, they can only

selectively attenuate signals in different frequencies, and part

of the energy in the input signal is changed to heat. They

correspond to infinite impulse response (IIR) filters in the

digital domain where the filters are always active and need a

power supply. Systematic design of passive filters was

started by Butterworth (1930).

The systems at the next level are simple dynamic systems

or clockworks which make periodic predetermined changes.

They are simple automata since they do not need manual

intervention after they have started their operation. The

clockworks form the basis for all synchronous computing

systems. Even when a computing system is in a sleep mode,

it still needs a clock to be able to wake up. The second level

includes also some simple machines. In general, a machine

is a system that changes energy to some mechanical

movement. In computing, a machine is an open system with

an internal state whose next state depends on the state of the

environment and the earlier internal state [71]. Hence, all

machines are state machines and in practice finite state

machines because of their finite memory. A motor is a

machine that turns energy into work in the form of rotating

motion. A motor is a clockwork and may include a gear for

manual control. Historically, a mill is a machine that takes its

energy from a stream of water or wind.

Control systems are automata acting upon controlled

variables to eliminate the effect of disturbances [349].

Examples include a thermostat and a PID controller. The set-

point value is fixed and given by a human operator.

Automatic systems are usually based on feedback and they

do not need any manual intervention [126], [349].

Adaptive systems need a performance criterion and they

eliminate the effect of variable disturbances on the

performance by using an algorithm in the feedback loop

[349]. The fixed set-point value is replaced by a reference

signal that is also called a desired or training signal.

Typically, a reference signal with a uniform spectrum is

needed if the system has some frequency-selectivity.

Adaptive receivers may be in a decision-directed mode. In

that case, the decisions of the receiver replace the external

reference signal. Some algorithms are blind in the sense that

they can generate their reference signal from the received

signal by using some kind of nonlinearity [208]. Sometimes,

adaptive systems are called “smart” [5].

Learning systems are adaptive systems that include

memory [128]. In the literature, also the term cognitive is

commonly used for learning systems [58], [258]. Simon

defines learning as “any process by which a system improves

its performance” [350]. The goal is to make the system faster,

more accurate, or more robust. A learning system changes its

behavior based on past experience, whereas adaptive

systems behave always in the same way. In machine

learning, algorithms are classified into supervised,

unsupervised, reinforcement, and evolutionary learning

algorithms [166]. Supervised learning is using a reference

signal just like in adaptive systems. Unsupervised learning

may be based, e.g., on pattern recognition. Reinforcement

learning is conceptually between supervised and

unsupervised learning where optimization is based on

maximizing some numerical reward, representing some

performance metric (Fig. 12). An example of reinforcement

learning is Q-learning. Reinforcement learning algorithms

may use positive feedback in addition to the conventional

negative feedback [136].

16

FIGURE 12. Reinforcement learning as a special case of the control loop in Fig. 5.

Autonomous and intelligent systems are advanced forms

of learning systems. Autonomous systems are learning

systems that do not need any external reference signal [48],

[49]. The unsupervised, reinforcement, and evolutionary

learning algorithms are autonomous. Decision-directed and

blind algorithms used in communications are simple

autonomous algorithms [208].

Intelligent systems are advanced autonomous systems

where the concept “intelligent” has many different

definitions [164]. We use those in control theory and

artificial intelligence. Legg and Hutter’s general definition is

a good starting point [164]: “Intelligence measures an

agent’s ability to achieve goals in a wide range of

environments.” The authors in [164] have summarized 10

definitions and 52 statements on intelligence. Some authors

use a broad interpretation that all systems from control

systems to self-organizing systems have a different level of

sophistication regarding intelligence [11]. In general, we

expect some form of autonomy and therefore learning

capability from intelligent systems [6]. Human intelligence

is a result of evolution and therefore also self-organization

[11].

In control theory, intelligence is defined as [46] “the

ability of a system to act appropriately in an uncertain

environment, where appropriate action is that which

increases the probability of success, and success is the

achievement of behavioral subgoals that support the

system’s ultimate goal. Both the criteria of success and the

systems ultimate goal are defined external to the intelligent

system. For an intelligent machine system, the goals and

success criteria are typically defined by designers,

programmers, and operators. For intelligent biological

creatures, the ultimate goal is gene propagation, and success

criteria are defined by the processes of natural selection.”

In artificial intelligence, a rational agent is defined as

“any device that perceives its environment and takes actions

that maximize its chance of success at some goal” [6].

Furthermore, “computer agents are expected to --- operate

autonomously, perceive their environment, persist over a

prolonged time period, adapt to change, and create and

pursue goals. A rational agent is one that acts so as to achieve

the best outcome or, when there is uncertainty, the best

expected outcome.” The definition in artificial intelligence,

regarding computer agents, is close to that of control theory.

In control theory, the environment is always assumed to be

uncertain, whereas in artificial intelligence, the environment

may be also perfectly known. For example, the traveling

salesman problem is complex, but there is no uncertainty.

Complex deterministic systems may appear stochastic or

probabilistic if the environment is only partially observable

[6]. In artificial intelligence, the goals may be set by the

system itself, whereas in control theory, the goals are defined

external to the intelligent system.

Uncertainty means lack of precise knowledge [8]. For

example, a mobile wireless channel is random and initially

unknown [351]. Uncertainty can be divided into epistemic or

knowledge uncertainty and aleatory or probabilistic

uncertainty. Epistemic uncertainty can be reduced by sensing

or measuring some variables of the system. In general, this

type of uncertainty can be reduced by simply obtaining more

information. Aleatory uncertainty, on the other hand, is

related to natural changes in nature which cannot be

controlled or eliminated. Aleatory uncertainty cannot be

reduced by obtaining more information. It may be too costly,

time consuming, or technologically infeasible to make the

observations, or the quantity in which we are interested, such

as the probability of a rare event or condition occurring in

the future, cannot be observed.

Control systems (Fig. 5) can effectively cope with

epistemic uncertainty. They sense the environment, i.e.,

observe the outputs of the process, and the feedback loop

provides this information to the decision block. Starting from

learning systems, one is able to model aleatory uncertainty

by performing statistical analysis of environment and system

states. Learning systems can store information about the

observed phenomena in their memories and can infer, yet

unobserved, phenomena in the future.

Self-organizing systems are autonomous learning

systems that are able to change their structure, in addition to

the behavior (Fig. 7). Self-organization or spontaneous order

refers to structural changes that do not need an external

control unit. Sometimes, such systems are called self-

restructuring [20]. In computer science, they are called

autonomic computing systems [50] and multiagent systems

[187] and in communications, they are called self-organizing

networks [222]. Many biological organisms are based on

self-organization through evolution and development of an

embryo.

The term self-organization was originally developed in

biology where the self-organization is “a phenomenon in

which system-level patterns spontaneously arise solely from

interactions among subunits of the system” [352]. The author

in [352] summarized ten definitions of self-organization

since the year 1947. Different authors have different

opinions on whether the interactions must be local or

whether the basic requirement is that there is no external

control unit. We use the latter more general definition.

Similar concepts are stigmergy and self-assembly, which

cannot be clearly separated from self-organization. In

stigmergy, the interactions are indirect through the

environment, for example temperature. This is a form of

global interaction. Stigmergy has various applications in

robotics, multiagent systems, and communication networks

[353]. Stigmergic communications was invented by Dorigo

in 1992. It is now also called ant colony optimization, which

is an example of swarm intelligence. It allows for very

efficient distributed control and optimization in a variety of

problem domains. In self-assembly, order is created by local

interactions for example between molecules. Self-assembly

is usually static. The system approaches an equilibrium by

17

reducing its energy. Dynamic self-assembly is usually called

self-organization. Self-organization may or may not include

positive feedback in addition to negative feedback [352].

Self-organizing systems can be based on centralized,

decentralized, or distributed control (Fig. 9). Some of the

first self-organizing communication networks were

distributed [354]. Such networks consist of a set of node

clusters, each node belonging to at least one cluster. Every

cluster has its own cluster head which acts as a local

controller for the nodes in that cluster. In one extreme, self-

organizing networks can be decentralized, so that even the

nodes work autonomously using for example swarm

intelligence [224]. In computer science, the term self-

management includes different autonomic properties [50],

[47]. Interacting cooperative systems are the most advanced

form of self-organizing systems because of possibly

conflicting goals [25]. Each society member is a learning,

autonomous, or intelligent agent or robot that must follow

certain laws, so that it does not do any harm to other entities

in its environment. Societies of autonomous agents or actors

are sometimes called value-laden systems with a culture

based on values [41]. Values such as trust or fairness are

needed in addition to laws to enable cooperation safely. An

essential concept in cooperating systems is collision

avoidance, which must be used also in communication

systems [15]. Autonomous agents may even teach each

other.

E. BASIC RESOURCES

We explain the basic resources using the open system

concept presented in Fig. 13 [71], [7], [4]. A system is

defined by the observer with its boundaries [20], and it can

be for example a control system or an actor (Fig. 5). The

three basic flows through all systems include materials,

energy, and information. Systems can be classified into

apparatuses (main flow is materials), machines (main flow is

energy), and devices (main flow is information) [356]. Parts

of those resources are transformed into waste, following

entropy law, and they are available for recycling.

FIGURE 13. Open system concept and basic resources that include materials (M), energy (E), information (I), time, frequency, and space.

Information includes data and control information [7]. It

is not an independent resource, but some energy is needed to

carry it. Transmitting information needs a certain bandwidth

and time which is perceived as delay. Information is

considered the third fundamental quantity in addition to

materials and energy [355]. Information can be presented as

a five-level hierarchy whose levels are from bottom up:

statistics (statistical information about symbols), syntactics

(information about the relationships between symbols),

semantics (information about the meaning of symbols in

reality), pragmatics (information about the practical effect of

symbols in reality), and apobetics (information about the

purpose of symbols in reality). The symbols may be, for

example, words. The hierarchy was originally developed by

Morris (1938), excluding the lowest and uppermost levels.

The system needs some space within its boundaries.

Therefore, the three additional basic resources are frequency,

time, and space. In electronics, the spatial limitation

including the form is often called form factor. All of the basic

resources are summarized in Table II.

TABLE II. Basic resources

Complexity of a system is in general defined as the

number of parts and interconnections within the system. In

engineering, we need a more fundamental definition.

Complexity may be defined as the amount of basic resources

needed to build the system up (these are called capital costs)

and the amount of basic resources used during operation

(these are called operational costs) [357]. In computer

science, computational complexity is defined by the time and

space requirements of algorithms [13]. Energy consumption

is also an important complexity parameter [86].

F. FUNDAMENTAL LIMITS

Exponential trends in performance requirements [65] are

extremely fast and we will soon approach some fundamental

limits of nature, which can be seen as walls beyond which

we cannot go. They form also constraints to our designs.

Some of the fundamental limits of nature are listed in Table

III. We have included only those, which are most interesting

for our purposes. There are good summaries available

elsewhere [40], [271], [174], [69] and additional references

are included in [66] where some special issues of journals are

mentioned.

TABLE III. Fundamental limits and principles

According to the energy conservation law, the energy

does not disappear anywhere but is changed into other forms.

The entropy law states that the available energy is reduced

and new energy is needed all the time. In general, heat has

the largest entropy. This causes heat transfer problems since

the heat must be removed to the environment, so that the

temperature of the components does not increase too much,

otherwise the components may be destroyed [358]. In

addition to a data flow, our system includes an energy flow

from energy source to energy sink, and cooling is based on

conduction, convection, or radiation. The energy density of

batteries is measured in J/dm2 or J/kg and it is increasing

quite slowly, in the order of 50% in ten years [359]. The

cooling efficiency (in W/cm2) is also increasing slowly and

there are fundamental limits for it [360]. According to the

present understanding, the maximum cooling efficiency is

0.25 - 1 W/cm2 with free air or water convection and up to

150 W/cm2 with forced water cooling [358].

In practice, the clock frequencies on a processor chip

were limited to 4 GHz in about 2004 mainly because of

power consumption limitations [361]. The maximum power

in hand-held terminals is 3 W because of cooling problems,

and the maximum power in single-chip processors is 200 W

[362], [363]. The maximum transmission power in hand-held

terminals is 200 mW because of safety reasons.

18

When the clock frequency was limited, the computing

speed could be still increased by parallel processing, but the

maximum speed-up has also theoretical bounds [364], [365],

[366]. The speed does not increase linearly with the number

of parallel processors.

The absolute zero is the lowest temperature in the Kelvin

scale. In practice, electronics is usually working at the room

temperature (290 K), and it would be expensive to reduce the

temperature to reduce the thermal noise level [367]. Thus,

thermal noise limits the energy efficiency of charge-based

electronics.

The most elementary operation is a switching operation

with the energy 𝐸sw, which must fulfil the inequality 𝐸sw/𝑁0

≥ ln 2, which is the Szilard limit [368]. The noise power

spectral density is 𝑁0 = kT, where k is the Boltzmann

constant and T is the absolute temperature [369]. The limit

was originally derived by Szilard (1929) and generalized by

Brillouin (1953), but it is also called the Boltzmann or

Landauer limit [369], [368]. We can derive the maximum of

the computing rate 𝐶sw (in logic operations per second) for a

given power consumption 𝑃sw. Since 𝑃sw = 𝐸sw𝐶sw, we

obtain 𝐶sw/𝑃sw = 1/𝐸sw, which is in the limit 𝐶sw/𝑃sw =1/𝑁0 ln 2. In practice, due to noise, we must be well below

this limit to guarantee high reliability in the computing [67].

In addition, electronics is limited by the Heisenberg limit

because of quantum effects [369]. The Szilard and

Heisenberg limits give a lower bound for the gate length of

a transistor. The theoretical lower bound for the gate length

is 4 nm, which corresponds only to about 20 silicon atoms

when we take the lattice structure into account. Because of

high manufacturing costs, the practical lower limit is 10 nm

for silicon transistors [370], [66]. With other materials, the

gate length can be smaller since the Heisenberg limit can be

reduced with a heavier effective carrier mass [371].

Although the transistors would be smaller, the Szilard limit

cannot be surpassed with irreversible or noninvertible

computing and the cooling problems would be larger

because of larger power density [360]. Reversible computing

is not seen as a practical approach for our purposes [368].

Thus, Moore’s prediction is not expected to continue for

silicon transistors after about 2021 [66].

A similar limit called the Shannon limit (1949) exists in

communications for the received energy per bit 𝐸b. It can be

derived from the Shannon channel capacity equation. The

ratio 𝐸b/𝑁0 is often called signal-to-noise ratio per bit, which

for reliable transmission must fulfil the inequality 𝐸b/𝑁0 ≥

ln 2, which is the Shannon limit. For an infinite bandwidth

and finite received power 𝑃, the capacity 𝐶 achieves its

maximum value, which is in a normalized form 𝐶/𝑃 =1/𝑁0 ln 2 [208]. A similar result was derived above for the

maximum computing rate.

For unbiased estimators, a lower bound called Cramer-

Rao lower bound exists, depending on the available signal-

to-noise ratio and the waveform. Since in communications

each bit and symbol has a limited signal-to-noise ratio, the

Cramer-Rao bound must be reduced by using the energy of

several symbols in the estimator.

The Gabor uncertainty principle (1946) says that the

product of signal duration and bandwidth cannot be below a

certain limit that is in the order of one, and the exact limit

depends on the definitions of duration and bandwidth. Thus,

if we reduce the duration of a pulse, the bandwidth is

increased and vice versa. An earlier result was from Nyquist

(1928) giving the minimum bandwidth where the pulses do

not interfere with each other if sent serially. Each pulse can

carry several bits if the pulse has many discrete levels (i.e.,

carrier amplitudes or phases). The maximum bit rate for a

certain bandwidth in a noisy channel is given by the Shannon

capacity, which can be approached by suitable modulation

and channel coding methods. Optical imaging instruments

have the Abbe diffraction limit for resolution. Abbe–

Rayleigh rule says that the wavelength of light is

approximately the smallest distance between two points that

can be distinguished with a lens [69]. The limit has been

applied also for antennas. However, it has been later shown

that there are no theoretical limits for directivity of antennas

and a concept called superresolution has emerged [372].

The speed of light is the upper speed limit, which

introduces challenges in communication networks and on

chips. The speed of light corresponds with a delay of 1 s for

a distance of 300 m. Radio waves are reflected from flat

surfaces and this creates the problem with multipath

propagation and resulting fading, in addition to shadowing

caused by the obstacles. Fading is a result of multipath

propagation and the finite speed of radio waves. Typical

delay spreads are 100 ns for indoor and 10 s for outdoor

systems. The delay spreads are much larger than the period

of the carrier with typical frequencies larger than 1 GHz,

causing constructive or destructive fading depending on the

phase relationships in a dynamic situation. On a chip, the

delay of wires or metal interconnections are now more

significant than the delays of logic gates [373].

There are problems that computers cannot solve [40],

[174]. Some problems are intractable or even unsolvable.

Problems can be classified into three groups: decision

problems (for example, yes or no), search problems (e.g.,

seven bridges of Königsberg), and optimization problems

(finding the best solution from all feasible solutions). The

difficulty of problems can also be classified into three groups

[174], [374]: First, tractable problems have polynomial

complexity (P). Second, intractable problems are problems

with exponential or superpolynomial complexity, including

nondeterministic polynomial (NP) complete and NP hard

problems. Although no formal proof exists, it is a common

consensus that NP complete problems are not tractable.

Third, for unsolvable problems, no solution can be found

with any computer according to the Church-Turing

conjecture. A distinction between polynomial and

exponential complexity was made first by von Neumann in

1953 [171]. The tractability of polynomial complexity

problems was first expressed by Edmonds (1965).

Intractable problems can in principle be solved, i.e., the

algorithm will eventually terminate, but the solution may

take an enormous amount of time and the number of memory

19

elements may be also enormous. Many engineering

problems are resource allocation problems that are often

intractable. Solving a problem is in general more difficult

than verifying the solution.

NP problems are decision problems whose solution can

be verified in polynomial time, but the problems cannot

always be solved in polynomial time with a deterministic

algorithm. Nondeterministic algorithm means in practice

guessing. NP problems include, e.g., polynomial time (P)

problems and NP complete problems. Quantum computers

can solve some exponential complexity NP problems in

polynomial time, but it is likely that quantum computers

cannot solve NP complete problems in polynomial time.

NP complete problems are the hardest problems in the NP

class. The existence of NP complete problems was

discovered independently by Cook (published in 1971) and

Levin (published in 1973, but discovered earlier). This is

called the Cook-Levin theorem. As far as known, a solution

can be found only in superpolynomial time, usually

exponential time. Polynomial time approximations are

available, for example randomization, restriction,

parameterization, and heuristic. There are over one thousand

NP complete problems, for example traveling salesman

problem (shortest path problem), knapsack problem

(resource allocation problem), timetable design, and

computer circuit fault detection problem [174]. For these

problems, the resources needed to find a solution are not

known, thus one can only compare the most recent result

with earlier results and continue solving until no significant

improvement can be obtained any more.

NP hard problems are a more general class of hard

problems, with NP complete problems as a special case.

Solutions cannot be always verified in polynomial time and

thus not all NP hard problems are NP problems. For example,

halting or termination problem is NP hard but not NP

complete. The halting problem is about whether a given

program halts for a given input in a finite time.

The analytical or deductive methods are not without

limitations. Gödel’s incompleteness theorem says that there

are always theorems which can neither be proved nor

disproved [40]. Often, the theorem is interpreted so that there

is no axiomatic system that would cover the whole

mathematics. The theorem is equivalent to the statement that

we can never tell whether a program is the shortest one that

will accomplish a given task [176].

Some deterministic feedback systems are chaotic and

thus unpredictable systems if they include a nonlinearity,

possibly caused by quantization, in the feedback loop [40].

The behavior of such systems is sensitive to initial

conditions. The algorithms based on feedback may converge

to a limit cycle or hang-up. Noise may be intentionally used

in the loop to avoid the limit cycle. The use of noise is

sometimes called dithering. In communications, repeated

collisions can be avoided by using random transmission

intervals [15]. A chaotic situation may also occur when many

adaptive loops interact with each other [262], for example, in

adaptive transmission and reception loops, or in a network

using transmitter power control. Generally, such situations

can be avoided by decoupling the interacting control loops.

Common solutions are to use centralized control or widely

different convergence speeds for different control loops.

G. MULTIPLE-CRITERIA DECISION-MAKING (MCDM)

Fundamental limits form constraints to our design. When we

are far from the limits, each objective can be optimized

independently without affecting too much the other

objectives. MCDM becomes relevant near the fundamental

limits [62], [113], [114]. From the MOO theory, we know

that the optimum, which is called Pareto optimum, is not

unique, but it gives the optimal trade-off between the

objectives. To make the final decision, we must rely on

subjective preferences. There is a significant number of

algorithms to find the optimum, each having their strengths

and weaknesses. The description of those algorithms is

outside the scope of this review. Interested reader is referred

to [8], [114] for more details.

Different algorithms are feasible for the different

hierarchy levels in Figs. 8 and 11. Selection criteria for

adaptive algorithms are summarized in [134]. The same

criteria can be used also for learning algorithms. Important

criteria include stability and rate of convergence, tracking

ability in a dynamic environment, and computational

complexity. In general, the algorithms are based on feedback

and their convergence speed depends on the number of

degrees of freedom in the optimization problem. The

algorithms can only track slow changes due to a trade-off

between noise and lag error [208].

In self-organizing control, heuristic MCDM algorithms

are often used when the structure must be changed [63].

MCDM algorithms can select the lower level algorithms and

decide some parameters for those algorithms. The selection

of algorithms for MCDM depends more on the form of

preference representation, whether objective functions are

linear or nonlinear, “shape” of feasible region (convex vs.

nonconvex), and, most importantly, their computational

complexity. For many of the engineering problems, the

feasible set is nonconvex with a nonlinear objective function

[62], [8]. Thus, heuristic methods (i.e., evolutionary and

genetic algorithms) have been popular. To guarantee global

convergence, genetic algorithms can be modified by using

immigration in addition to mutations [5]. High immigration

rate forces the algorithm towards random search and low

immigration towards a genetic algorithm. The classical

approach is to use scalarization methods, that is, a

multiobjective problem is reduced to a single-objective

problem by combining the objectives [62]. This works well

if the multiple objectives are somewhat independent and

monotonic, and the set of all possible solutions is convex. In

some cases, grid search could be a solution [295]. As an

example, optimization of the weights of a finite impulse

response (FIR) filter is known to be a convex problem, but if

the filter is an IIR filter, the problem is in general nonconvex

[133], [375]. Furthermore, IIR filters may experience

20

stability problems unless the optimization problem is

constrained in such a way that poles of the transfer function

lie inside a unit circle in the z-plane.

IV. MANIFESTATION OF SOME MULTIDISCIPLINARY CONCEPTS IN COMMUNICATION NETWORKS

In this section, we briefly summarize the state of the art from

the perspective of communication networks. The more

advanced interdisciplinary and transdisciplinary views [42]

remain for later work, as discussed in the introduction.

A. KEY PERFORMANCE INDICATORS (KPIS)

We will use examples of KPIs from communication systems

since in that discipline comprehensive analyses have been

made for several generations of mobile cellular systems

[376], [65]. The network-induced constraints in networked

control systems are described in [140]. The first generation

cellular systems were introduced in the beginning of the

1980’s, and a new generation has been introduced every ten

years. The networks of the 2010’s are thus called the fourth

generation (4G) systems, and those of the 2020’s are called

the fifth generation (5G) systems.

The network requirements are called performance

requirements or KPIs. Network performance must be

monitored and controlled through feedback (Fig. 5) [340].

Performance is defined as “the manner in which or the

efficiency in using the available resources with which

something reacts or fulfils its intended purpose” [377]. By

efficiency, we mean the ratio of benefits and expenditures [7]

where the benefits fulfil some user needs and expenditures

are some basic resources (Fig. 13). In communication

networks, the benefits are usually the correctly received data

bits and the expenditures are the other basic resources

including control bits. In computer science, the benefits are

operations consisting of elementary arithmetic operations,

including additions, subtractions, multiplications, and

divisions [378], [111]. There are also lower level operations

called logic operations in electronics and various higher-

level operations [13].

The metrics can be measured in any of the OSI layers

[15], [16]. In the application layer, subjective quality of

experience (QoE) metrics can be used in addition to the

metrics listed above [379]. When performance is measured,

we must define the layer, the corresponding PDU, and

whether the measurement is done one-way or round-trip.

The requirements of 4G and 5G systems are summarized

in Table IV [380], [65], [381]. Many of the KPIs are self-

explanatory. The requirements are developing exponentially.

For example, the network energy efficiency requirement is

increased by a factor of 100, so that the power consumption

per unit area is not increased when the area traffic capacity

is increased by the same factor.

Data rate requirements are measured in bit/s in the

application layer. The number of PDUs in second is often

called the throughput, often expressed in bit/s. The required

user experienced data rate should be available in at least 95%

of the locations, including at the cell-edge, for at least 95%

of the time within the considered environment. In computer

science, the throughput is measured in operations per second.

TABLE IV. KPIs and requirements in 4G and 5G communications networks

Spectral efficiency is the throughput normalized by the

bandwidth in a cell (in bit/s/Hz) [250]. Spectral efficiency

can be improved by using multilevel modulation and channel

coding methods and multiple antennas at the expense of

energy efficiency. The best known channel codes include

low-density parity check codes and polar codes. Network

energy efficiency refers to the quantity of information bits

transmitted to or received from users, per unit of energy

consumption of the network (in bit/J), including cellular

technologies, radio access and core networks, and data

centres. Energy efficiency in communications is thus the

inverse of the total energy per bit. Energy efficiency is the

same as power efficiency since 1 bit/J = 1 bit/Ws = 1 bit/s/W.

It is the throughput normalized by the power. Only data bits

are counted in the numerator of the energy efficiency

although all energy (data and control) is taken into account

in the denominator. Energy efficiency can in general be

improved by avoiding complex algorithms but usually this is

obtained at the expense of spectral efficiency. The

throughput normalized by the area is the area efficiency used

in electronics [382], which is called area traffic capacity in

5G systems. Radio Resource Management (RRM) and

Dynamic Spectrum Allocation (DSA) methods are crucial in

avoiding interference and in obtaining the maximum area

traffic capacity.

Similar normalized throughputs can be used also in

computer science when throughput is replaced by operations

per second. For example, the energy or power efficiency is

measured in operations/s/W [383] and its inverse is the

energy per operation.

Delay and latency are synonymous terms. They are

measured from the beginning of the transmission of a PDU

to the end of reception of the same PDU [384], [252]. Delay

includes processing, packetization, transmission, queuing,

and propagation delays. Total delay is also called transfer

delay or transit delay [385]. In the application layer, total

delay is called end-to-end delay. Processing delays are

caused by inefficient processing, for example interleaving

and ARQ. Packetization delay is incurred in filling up a

packet with data symbols. Transmission delay is caused by

serial transmission; it is the delay between the transmission

of the first and the last bits of a PDU. Queuing delays occur

when buffers in network devices become flooded.

Propagation delay is caused by the physical medium because

of the finite propagation speed of the electromagnetic waves.

Differently from most other metrics, delay is usually not

expressed as a ratio. However, we propose a normalization

with the smallest possible propagation delay using the speed

of light in vacuum for a given distance. In this case, delay

efficiency would be the ratio of the minimum propagation

delay and the actual total delay (in %). In computer science,

delay is measured for each operation.

21

The delay across the Internet can be on the order of 100

ms or even more depending on the physical distance [384].

Typically, computer users feel delays under 100 ms

unnoticeable. Conversations appear as real time two-way

communications when we receive the audio signal within 70

to 100 ms [32]. Lip synchronization between a video stream

and its soundtrack requires similar delays for video and

audio, otherwise the sound seems disconnected from the

movements on the video. In haptic communications, the

maximum delay should be in the order of 1 ms to avoid

cybersickness [31]. If the delay is limited to 1 ms, the

network radius must be limited to a few kilometers [243]. A

modern concept is the age of information by Kaul et al.

(2011) as a notion to characterize the freshness of the

knowledge about a process observed remotely [386]. The

age of information can be defined as the time interval from

the start of the transmission of the information to the present

time.

The concept dependability includes availability and

reliability [387], [388], [389]. Reliability is measured only

when the network is available [390]. The network

availability X is defined as follows: the network is available

for the targeted communication in X% of the locations where

the network is deployed and X% of the time. No numerical

value has been defined for availability in [65], [381], but in

[390] availability requirement is 99.999%. Availability is

improved if the robustness of the system is increased.

Reliability can be defined in many ways, and we present

the most common definitions. Reliability is usually defined

as the amount of sent PDUs successfully delivered to the

destination divided by the total number of sent PDUs [381].

Reliability is thus the complement of the error rate, i.e.,

reliability = 1 - error rate. An alternative is to define the

average interval between errors [391]. This may be more

fundamental from the user point of view than the error rate,

which does not directly tell the time distribution of errors

unless the throughput is given.

Because of different fundamental limits, there is a

fundamental trade-off between throughput, delay, and

reliability [201], [392] and therefore also between spectral,

energy, spatial, and delay efficiency, and reliability [208],

[392]. We have thus these five basic metrics. An example of

the trade-offs is computation-communication trade-off

[393], [66], [394]. In [393], the related trade-off between

operating and radiated energy is explained. Because of the

fundamental trade-off, it is reasonable to divide systems

according to the most critical performance metric. The

systems can be divided into bandwidth-, energy-, delay-,

space-, and reliability-limited or -sensitive systems [384],

[395], [396], [65].

B. HIERARCHY CONCEPT

Hierarchical control has been studied extensively in different

disciplines, as described in Section II. Hierarchical concepts

have played a significant role also in wireless

communications, such as in the form of OSI model [326],

[15], [16]. However, the interest of applying hierarchical

control paradigms to the rather complicated heterogeneous

wireless network architectures, providing a multitude of

different services, has emerged only recently. The future

networks must simultaneously support a massive number of

low data rate subscribers for sensing applications and a

number of ultrahigh data rate subscribers for high-definition

multimedia streaming applications. Augmented reality user

interfaces with large bandwidth and small latency might be

the first popular application for 5G. The main objective of

the required hierarchical arrangements is to find suitable

trade-offs between optimized performance criteria and

control complexity while allowing scalable network size. In

upcoming 5G heterogeneous networks, the RRM can be

done at different hierarchy levels, mainly reflecting the

selectable degree of centralization and resource allocation

resolution. We will next discuss these important topics more

closely.

Degree of centralization. A major debate in system

architecture discussions of modern wireless systems is the

degree of centralization of the underlying network control.

The RRM can be divided into centralized, decentralized, and

mixed (also semi-centralized, partially decentralized)

schemes [29], [64], [398]. Centralized schemes collect all

necessary information of a network to make all relevant

RRM decisions, while fully decentralized schemes use only

local information at the base stations and user devices.

The main trade-off between centralized and decentralized

control schemes reduces largely into 1) the required number

of internode iterations, i.e., iterations between control

decisions of separate network nodes to find an optimal

solution, 2) the amount of required control data per iteration,

and 3) the delay to convey information in each iteration.

Centralized schemes enable optimal decisions within a single

node without internode iterations, i.e., as one-shot decision

at the cost of large delay and excessive amount of control

data; whereas decentralized schemes may require a high

number of internode iterations every time the environment

changes. In general, a control loop (either open or closed)

may be required in all approaches because the environment

is changing over time or an iterative structure must replace a

one-shot solution that is too complicated to be resolved in

real time. Mixed schemes aim at finding suitable trade-offs

between the above three trade-off factors that fit the given

application requirements. A more detailed analysis of

finding the optimal degree of centralization in 5G networks

is found from [397], which includes both the centralization

of network control as well as centralization of signal

processing.

In mixed schemes, many features of centralized schemes

are used, but in this case, the role of the central unit is only

assistive or auxiliary [399]. In the past, the third generation

(3G) cellular networks were highly centralized, whereas in

4G networks, the degree of centralization is drastically

reduced, providing significantly more authority for

individual base stations to make RRM decisions [400]. In the

upcoming 5G networks, finding the optimal degree of

centralization is one of the key challenges where the location

22

of both the control units and server units are important [347].

The required control time resolution and delay requirements

of the controlled RRM parameters affect heavily the optimal

degree of centralization of the control architecture where the

degree should be adaptively modified. Typically, physical

layer variables are adjusted in a millisecond resolution,

whereas network layer variables can be adapted with orders

of magnitude lower time resolutions. The recent software-

defined networks further separate physical and logical

controllability [401]. Therein, network designers resort to a

physically distributed control plane, on which a logically

centralized control plane operates. In essence, the logically

centralized network view enables simplified models of the

actual distributed physical world but also results in

fundamental trade-offs between complexity and model

consistency.

Resource allocation resolution. Another important factor

naturally incorporating a hierarchical structure is the

resource allocation resolution for individual users and

network cells. In general, the higher the resolution, the better

the achieved optimization capability, but at the cost of higher

control complexity. For instance, in the 3GPP standards,

different terminal categories support different resourcing

resolutions that match the respective QoS requirements

[402]. In the 4G systems, the smallest resource block

allocated to users is 180 kHz in the frequency domain and 1

ms in the time domain. In the newest 3GPP releases, the

resolution is higher for narrowband Internet of Things (NB-

IoT) devices, so even as small as 3.75 kHz resource blocks

can be allocated to a single device to help to reduce the power

consumption with very low data rates [403]. Moreover, the

network virtualization concept is one of the most promising

approaches to implement efficient resource control for 5G

systems [404]. Therein, wireless virtual resources are

obtained by slicing network infrastructure into multiple

virtual slices that can be used very flexibly; independently of

the location or operator requesting it. Based on the

requirements, the virtualization can be done at the network

level, user flow level, packet level, subchannel level, or even

hardware level.

C. RESOURCE EFFICIENCY AND TRADE-OFFS

Efficient use of available basic resources (Fig. 13) is the

primary task of the RRM. The main objective of the RRM is

to control the use of available resources in such a way that

users’ QoS requirements are met and, at the same time, the

overall radio resource usage at a system level is minimized.

To this end, radio resource management uses different

mechanisms at different communication layers that include,

for example, channel quality measurement and reporting,

link adaptation, retransmission control, dynamic scheduling,

and management of QoS parameters.

The recent exponential growth in the volume of

transmitted data coupled with the high costs of energy

highlights the need to consider energy consumption of

communications equipment. Thus, efficient use of resources

requires a holistic approach to network planning, radio

resource management, and physical layer transmission.

Network planning. At the network planning stage,

designers typically face the task of balancing the amount of

basic resources needed to build the system up (i.e., capital

costs) and the amount of basic resources used during

operation (i.e., operational costs). To reduce capital costs,

network designers would prefer to cover the service area

with a few macro cells which, in turn, may lead to increased

energy consumption and thus higher operational costs. On

the other hand, increasing the number of base stations that

serve a given area, may decrease total energy consumption

but increase capital investments. These considerations lead

to a conclusion that a mixed deployment scheme, which

involves macro-, micro-, pico-, and femtocells, may be

needed to balance these two types of costs. The

corresponding maximum cell radius is 35 km, 2 km, 200 m,

and 10 m, respectively [405]. Other possibilities include

introduction of so-called sleep modes in base stations and

deployment of cooperative networks with distributed

antenna systems that consist of many low-cost and small-

coverage units. A major engineering challenge would be the

control of such a network and ensuring its resource

efficiency. We conjecture that such networks would need to

be capable of self-organization and require distributed

control.

Radio resource management. Dynamic scheduling is the

main mechanism of optimizing the use of resources with

respect to the requirements of upper layers. A dynamic

packet scheduler is responsible for allocating resource blocks

in time and frequency and assigning the modulation and

coding scheme. In addition to that, a dynamic scheduler

needs to cope with channel and traffic uncertainty.

One of the major challenges is to balance the average

end-to-end service delay versus average energy consumed in

the transmission. A mathematical model describing

relationships between end-to-end delay and average

consumed energy is quite difficult to create because it should

involve information and queueing theories and uncertainties

both in channel and traffic patterns. MCDM under

uncertainty could be used to obtain a set of reasonable pairs

of end-to-end delay and energy consumption with some

advanced learning system acting as a decision maker.

Another major challenge in radio resource management

is to balance efficiently the control or signaling and data

symbols. The present wireless networks rely on separation of

control and data symbols which seems not optimal from the

energy efficiency point of view. In general, studies on

resource allocation between control and data symbols are in

their initial stages. However, limited literature results are

available for point-to-point links. MCDM could be used to

find a reasonable balance between control and data bits with

some advanced learning system acting as a decision maker

and making the final selection.

Physical layer transmission. Many concepts of control

and adaptive systems have already been applied to

communication links, for example, AGC, AFC, AMC.

23

However, many trade-offs still require further studies, for

example, the achievable bit rate versus energy consumption

or used bandwidth versus power needed for transmission.

One of the major difficulties is the lack of sufficiently

accurate models of hardware energy consumption, which

precludes accurate estimation of energy consumption for

different modulation and coding schemes. Other difficulties

arise when one wishes to study multiuser and multicell

scenarios where the achievable link spectral efficiency of a

given link depends on interference conditions.

We expect that autonomous radios and networks, which

are a form of learning systems, will be capable of achieving

reasonable balance between used bandwidth and power

needed for transmission due to their inherent capability of

flexible use of available spectrum. Furthermore, cooperative

communication, which is again a manifestation of learning

systems, will help to balance spectral and energy efficiency.

V. CONCLUSION

We started this work with emphasis on control theory since

it is the origin of many of the concepts that are relevant for

intelligent and resource-efficient systems. Our work will

continue in the future by integrating a large body of artificial

intelligence research into the conceptual model. In control

theory, the basic system models are autonomous and remote

control systems; in computer science, the basic system model

is distributed self-organizing computing; and in

communication theory, self-organizing networks. We further

note that in addition to the normal divergent trend in

research, there is also a converging trend, leading to similar

concepts in different disciplines. Cyber-physical systems in

computer science and Tactile Internet in communication

theory can be seen as different forms of networked control

systems with haptic feedback, available in control theory in

a rudimentary form since 1966, and its roots in teleoperation

as far as 1945. They are also examples of telepresence, an

idea that was presented in 1980. We can also use the newest

theories of brain studies, morphogenesis, and second-order

cybernetics to improve self-organizing systems. We also

note that two independent tracks exist in brain studies, one in

neuroscience and another one in robotics. It would be very

useful to combine these efforts.

Future computing and communications systems need to

be based on intelligent use of resources. Since we are

approaching the fundamental limits in various fields, the

future development must be based on multiobjective

constrained optimization which can identify an optimal

combination for the use of multiple scarce resource types.

However, maintaining exponential predictions, such as those

of Moore and Keyes, is questionable. The limited basic

resources in most disciplines include materials, energy,

information, time, frequency, and space. Major

requirements, especially in communications include

throughput, delay, and reliability, which are mutually

conflicting. When throughput is normalized with bandwidth,

power, and area, we obtain spectral, energy, and area

efficiency, respectively, that are also mutually conflicting

objectives. Control overhead is properly taken into account

in these metrics when it is included only in the expenditures

and the throughput includes only the data bits.

The society expects increasingly higher research impact,

which calls for multidisciplinary approaches in order to solve

efficiently the demanding problems at hand. Relevance of

research can be enhanced by systems thinking. We presented

an extensive bibliography of earlier reviews and books in

general system theory, decision theory, control theory,

computer science, and communication theory. Our

chronology covers the last hundred years. To support

conceptual analysis, we presented a modern hierarchy of

human-made systems. The hierarchy includes static, simple

dynamic, control, adaptive, learning, and self-organizing

systems. Autonomous and intelligent systems are advanced

forms of learning systems. Self-organizing systems may

consist of cooperating agents or robots so that they form a

society.

Agents are hierarchical systems that are divided into

reactive, proactive, and interacting (competitive or

cooperative) agents, and their mixed forms. Proactive

systems are able to do planning; to predict the future and

select the best course of action to achieve the goal. These

ideas are useful also in communications theory. The use of

abstraction levels is mandatory in complex systems, in

communications theory in the form of network graphs.

We observed that feedback (for learning), hierarchy (for

managing complexity), degree of centralization (for

scalability), optimization (for efficient use of resources), and

decision-making (for subjective preferences when the

optimum is not unique) form the cornerstones of future

systems. In communications, the highest potential for highly

intelligent systems is in hierarchical and distributed resource

management using different time, frequency, and spatial

resolutions at each hierarchy level. The hierarchy has been

shown to be optimal in minimizing entropy. Not only

spectrum or energy are seen important resources, but also the

other basic resources such as time and delay. The intrinsic

value is not the intelligence itself but the hierarchy of

systems providing different levels of autonomy. The

hierarchy level must be selected according to the needs since

the higher in the hierarchy of systems we are, the higher

complexity is needed. In addition to closed-loop iterative

feedback systems, we also need possibly approximate and

suboptimal open-loop and one-shot solutions because of the

challenges with convergence time in the iterative systems.

The convergence time is not in general easy to predict and

depends on the number of degrees of freedom.

The multidisciplinary approach is demanding and it is not

reasonable that all researchers are educated as generalists.

Rather, the efficiency of our society is based on division of

work, and the experts should be coordinated with common

goals. This calls for systems thinking and knowledge

exchange between disciplines. Information acquisition tools

representing knowledge from multiple disciplines could

advance such efforts. The tools should be based on a unified

terminology and definitions for the disciplines in question.

Such a terminology and definitions are important for

24

personal communication between specialists in different

disciplines as well.

Our multidisciplinary view is the first step towards the

most advanced transdisciplinary view, which can be called

systems view. We will continue this endeavor with more

detailed conceptual analysis in order to produce a more

complete and more unified conceptual framework that

combines the bodies of knowledge from the five disciplines.

Our aim is that researchers form different disciplines can

place their efforts in this framework and identify the related

research that can help them to generate new knowledge that

advances the development of intelligent and resource-

efficient systems. The general, long-standing problem to be

tackled is optimization and distributed decision-making in an

uncertain, dynamic, and nonlinear environment with

mutually conflicting objectives.

ACKNOWLEDGMENT

Comments from Heikki Ailisto, Jarmo Alanen, Tapio

Frantti, Tapio Heikkilä, Hannu Heusala, Jyrki Huusko,

Marko Höyhtyä, Juhani Iivari, Pertti Järvensivu, Laszlo B.

Kish, Kari Leppälä, Teemu Leppänen, Ilkka Norros, Susanna

Pantsar, Pertti Raatikainen, and Alexandre Valentian are

gratefully acknowledged.

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AARNE MÄMMELÄ (M’83–SM’99) received

the degree of D.Sc. (Tech.) (with honors) from the University of Oulu in

1996. He was with the University of Oulu from 1982 to 1993. In 1993, he joined VTT Technical Research Centre of Finland in Oulu. Since 1996, he

has been a Research Professor of digital signal processing in wireless communications. He has visited University of Kaiserslautern in Germany in

1990-1991 and the University of Canterbury in New Zealand in 1996-1997.

Since 2004, he has been a Docent (equivalent to Adjunct Professor) at the University of Oulu. He is a Technical Editor of the IEEE Wireless

Communications and a member of the Research Council of Natural Sciences

and Engineering in the Academy of Finland, which is funding basic research. He has given lectures on research methodology at the University

of Oulu for over 15 years, including the systems approach in addition to the

conventional analytical approach. His research interests are in general system analysis, philosophy and history of science and engineering,

adaptive and learning systems, and resource efficiency in communications.

33

JUKKA RIEKKI (M’92) received the D.Sc. (Tech.) degree from the Department of Electrical Engineering, University of Oulu,

Finland, in 1999. Since 2001, he has been a professor of software

architectures for embedded systems at the University of Oulu and since 2014 the dean of the Faculty of Information Technology and Electrical

Engineering. He visited Intelligent Systems Division, Electrotechnical

Laboratory, Tsukuba, Japan in 1994–1996. In his PhD thesis, he studied

reactive task execution of mobile robots. In 2000, he started to study

context-aware and ubiquitous systems. In recent years, he has shifted his

research focus on Internet of Things and edge computing, emphasizing efficient application of Artificial Intelligence and Semantic Web

technologies in distributed and resource-limited systems. He has been

advisor of 9 doctoral theses. He has published 40 journal papers, 6 book chapters and over 160 conference papers.

ADRIAN KOTELBA (M’08–SM’15) received the D.Sc. (Tech.) degree from the Department of Electrical and Information

Engineering, University of Oulu, Finland, in 2013. He works as a Senior

Scientist at VTT Technical Research Centre of Finland Ltd. His personal interests include communications theory, adaptive signal processing,

adaptive modulation and coding, cross-layer design, cryptography, and

physical layer security. He has participated in a number of research projects

related to modelling of wireless multi-antenna channels, multimedia transmission in cellular networks, adaptive radio resources allocation and

adaptive signal processing, including adaptive compensation of nonlinear

amplifiers, and security of wireless networks. His doctoral thesis, published in 2013, was about application of decision theory, including multiple-

criteria decision making and multi-objective optimization with variational

methods, to adaptive transmission in wireless fading channels.

ANTTI ANTTONEN (M’10–SM’14) received

the D.Sc. (Tech.) degree from the Department of Electrical and Information

Engineering, University of Oulu, Finland, in 2011. He works as a Senior Scientist at VTT Technical Research Centre of Finland Ltd. He has made

longer research collaboration visits to Lucent Technologies (USA) in 2000,

University of Hannover (Germany) in 2008, and University of Leuven (Belgium) in 2014. He has participated in numerous national and European

research projects related to wireless communications. Dr. Anttonen is a

Senior Member of the IEEE. His main interests include energy efficient transmission algorithms (physical layer, decision theory) and cooperative

smart resource management (network layer, communications and queueing

theory) for personal area and cellular networks. His doctoral thesis

published in 2011 was about designing resource-efficient and low-

complexity short-range communication transceivers. He has further

published several scientific journal papers related to energy efficiency, energy sustainability, and delay sensitivity.

34

Control information

Sensing information

(a)

StateProcessSense Decide Act

Feedback

Information exchange

Noise

(b)

FIGURE 1. A control loop. a) An example where a ship is remotely controlled. b) A general control loop where the system to be controlled is called a process. Solid lines with arrows denote control signals and dashed lines with arrows denote sensing signals. The sense, decide, and act blocks close the loop through the process that is often called the plant.

1990 2000 2010 2020 2030

Autonomic networks

Communication theory

Timeline

Computer science

Autonomic computingCyber-physical systems

Control theory

Networked control systems

Kinesthetic feedback

Multiagent systems

Remote control

Self-organizing networks

Distributed computing

Time sensitive networks

Mobile agents

1980197019601950

Autonomous robots

Automatic systems

Artificial intelligence

Cellular systems

Local area networksBroadcast networks

Autonomic robots

Haptic feedback

Embedded systems

FIGURE 2. A simplified chronology of the most advanced systems that combine efforts from control theory, computer science, and communication theory.

D D

Goal Route Goal

Manually-controlledsystem

Automaticsystem

Autonomoussystem

FIGURE 3. Manually-controlled, automatic, and autonomous systems. A self-organizing system is an advanced form of an autonomous system.

35

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030

Self-organization

Cybernetics

General system theoryFundamental limits General system theory

Timeline

Open systems

Adaptation

Second order cybernetics

Complexity theories

Brain models

Morphogenesis

Bionics

System of systems

Decision theory

Game theory

Pareto optimum

Linear programming

Statistical decision theoryDynamic programming

Genetic algorithms

Nonlinear programming

Graph theory

Games with incomplete information

Operations research

MOO, multi-objective optimizationMCDM, multiple-criteria decision

making

MOO MCDM

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030Timeline

Autonomous control

Control theoryStability analysis

Adaptive control

Learning control

Feedback

Intelligent control

Decentralized systems

Networked control systemsRemote control

Hierachical control systems

Robotic arm

Microrobots

Cooperating robotsMobile robots Bipedal robots

Haptic feedbackIdea of robot

Self-organizing control

Idea of telepresence

Robot architectures Proactive architectures

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030Timeline

Computer scienceArtificial intelligence

Machine learning Computational intelligence

Autonomic computing

Expert systemsSoftware agents

Mobile agents

Cyber-physical systems

Multiagent systems

Complexity analysis

Distributed computing

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030Timeline

Embedded systems

Time sensitive networks

OSI Open Systems InterconnectionAGC automatic gain controlPLL phase-locked loopAFC auomatic frequency controlDLL deloy locked loopTPC trasmitter power controlARQ automatic repeat requestAMC adaptive modulation and codingDCA dynamic channel assignmentRRM radio resource managamentDSA dynamic spectrum allocation

Hierachical systems

OSI model

DSA

Self-organizing networksAd hoc networks

Autonomic networks

Communication theory

Internet ProtocolPacket radio systems

TPC

Cognitive radiosPLL

Network awareness

AGCEqualizationDLL

AFCAMC

Rake

Beamforming

ARQ

DCA RRMInternet of skills

Software-defined networks

Active networks

Packet switching

Network traffic monitoring

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030Timeline

Tactile Internet

FIGURE 4. Chronology of intelligent resource-efficient systems. The concepts and acronyms are explained in the text. The date of the first occurrence of a concept in the literature is indicated with the position of the first letter of each term.

36

Sense Act

P

D

Noise

Information exchange

Environment

Agent

FIGURE 5. The control system in Fig. 1b in an alternative form. D refers to the decision block and P refers to the plant or process.

D

Nested hierarchy

P

D D

Layer hierarchy

P

D

Dominance hierarchy

D

P

D D

FIGURE 6. Three common hierarchies.

Structural level

Behavioral level

Functional level

Description levels

Physical level

FIGURE 7. Typical abstraction levels of a system in different disciplines.

37

Controller

Coordinator

Manager(Organizer)

Low complexity, high speed (wide bandwidth), high accuracy, no memory, narrow spatial extent, control and adaptive algorithms

Short-term memory, learning algorithms

High complexity, low speed (small bandwidth), low accuracy, long-term memory, wide spatial extent, autonomous and self-organizing algorithms

Hierarchy level

Properties

Behavioral layer: Responsible for movement, avoiding obstacles, closest to sensors and actuators.

Executive layer: Coordinates the behavioral layer, responsible for choosing the current behavior (movement) to achieve a task.

Task-planning layer: Long-term goals of the robot within resource constraints, taking into account priorities, order of tasks, and recharging.

Example from robotics

Physical layer: bits or symbols transmitted

Data link layer: frames transmitted

Network layer: packets or datagrams transmitted

Example from communications

FIGURE 8. Hierarchical control in a layer and dominance hierarchy. Examples are given from robotics and communications.

38

Centralizedcontrol

Process

D D

D

Decentralizedcontrol

Distributedcontrol

Process

D

Process

D DD D

(a)

Process

D DD D

D

D D

(b)

FIGURE 9. Degree of centralization. a) Centralized, decentralized, and distributed control. b) Distributed control combined with dominance hierarchy using clusters of control units.

Centralized(automatic)

Degree of centralization

Central control unit used to force cooperation, sensing information proceeds upwards, control information downwards in hierarchy, also called automatic systems

Properties

Decentralized(autonomous)

Interacting competitive rational systems, may be reactive or proactive, sensing the environment, also called autonomous systems

Distributed(autonomic)

Interacting cooperative systems forming clusters, message passing or shared memory used to exchange information, sometimes called autonomic systems

FIGURE 10. Properties of systems with different degrees of centralization.

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1. Static systems Everything fixed Passive filter

2. Simple dynamic systems Periodic predetermined

changesClock

4. Adaptive systemsReference signal, performance

criterion, algorithm

Adaptive estimator, parameter identification,

LMS algorithm

5. Learning systems Memory, past experience Pattern recognition, Q-learning

6. Self-organizing systems Structure changesCooperating robots, self-

organizing networks, structural identification

Hierarchy level Properties Examples

3. Control systems Feedback, set-point value PID control

FIGURE 11. General hierarchy of systems in different disciplines according to the complexity of decisions. The structure changes only in self-organizing systems, otherwise the behavior changes, see Fig. 7.

State

P

D

Reward

FIGURE 12. Reinforcement learning as a special case of the control loop in Fig. 5.

M

E

IOpen system

M

E

I

Waste (recycling, cooling, interference)

M E I

Delay

Space

Bandwidth

Capital

Data and control

EnvironmentBoundary

FIGURE 13. Open system concept and basic resources that include materials (M), energy (E), information (I), time, frequency, and space.

40

TABLE I. Reviews and books in various disciplines

Discipline Reviews and books General system theory History [77], [71], [4], systems engineering [7], [78], multidisciplinary conceptual analysis [20],

systems of systems [79], adaptation and self-organization [80], [81], [82], cybernetics [83], [84], [85], fundamental limits [40], complexity in electronics [86], [87], [88], system biology [89], complexity theories [90], [91], [92], bionics (biomimetics) [93], [94], [95], [96], [97], brain models [11], [75], [76], [98], [99], [100], [101], [102], [103], [104], reaction times of human senses [105]

Decision theory History [106], [107], [108], [109], [110], general theory [9], multiple-criteria decision-making and multiobjective optimization [62], [8], [111], [112], [113], [114], game theory [115], [116], genetic and evolutionary algorithms [117], [118], [119]

Control theory History [120], [121], [122], [123], general theory [10], hierarchical and distributed control [63], [124], [125], [126], [127], [128], [129], [130], [131], adaptive and learning systems [132], [133], [134], autonomous and intelligent control [135], [5], [136], networked control systems [137], [138], [34], [139], [28], [140], [141], [142], [143], [144], remote control and haptic feedback [33], [145], [146], [35], [147], [148], [149], [150], [151], [152], [153], mobile robots and navigation [60], [25], [154], [155], [156], [157], [12], [158], [26], [159]

Computer science History [160], [161], [162], general theory [13], artificial intelligence [6], machine learning [163], [164], [165], [166], distributed computing [167], [29], [168], computational intelligence [169], [170], computational complexity [171], [172], [173], fundamental limits in computation [174], [69], [175], [176], augmented intelligence [177], ambient intelligence [178], [179], context awareness [180], [181], location awareness [182], multiagent systems [183], [184], [185], [186], [187], [188], [189], [190], [191], [192], [61], autonomic computing [50], [193], [27], [194], cyber-physical systems [195], [23], [196], [197]

Communication theory History [198], [199], [200], general theory [201], [202], [15], [16], [203], [204], [205], [206], [207], [14], [208], [209], graph theory [210], [211], [212], basic resources [72], software-defined networks [213], [214], [215], [216], [217], [218], [219], [220], ad hoc and self-organizing networks [221], [222], [223], [224], [225], [226], [227], [228], [229], [230], [231], adaptive, cognitive, and autonomous networks [51], [232], [233], [234], [235], [236], [237], [238], [239], context-aware networking [240], tactile and haptic communications [241], [31], [32], [242], [243], [244], [245], hierarchical network synchronization [246], [247], [24], hierarchical routing [248], [249], spectral efficiency [250], energy efficiency [251], reduction of delays [252], radio resource management and dynamic spectrum allocation [253], [254], [255], [256], [257], cognitive radios [58], [59], [258], [259], [260], [261], adaptive systems [262], [263], [264], bio-inspired networking and signal processing [265], [266]

TABLE II. Basic resources

Basic resource Other related terms Materials Energy Information Time Frequency Space

Mass Power Data, control Delay, latency Bandwidth, spectrum Size, area, volume

TABLE III. Fundamental limits and principles

Fundamental limit or principle Inventor Meaning Energy conservation law Entropy law Heat transfer Maximum speed-up Absolute zero Szilard limit (Landauer limit) Heisenberg limit Channel capacity Cramer-Rao bound Gabor uncertainty principle Abbe diffraction limit Upper speed limit Church-Turing conjecture Cook-Levin theorem Incompleteness theorem Unpredictable systems

Joule 1843 Carnot 1824, Clausius 1854 Avila et al. 2007 Amdahl 1967, Gustafson 1988 Kelvin 1848 Szilard 1929 Heisenberg 1927 Shannon 1948 Rao 1945, Cramer 1946 Gabor 1946 Abbe 1873 Einstein 1905 Church 1936, Turing 1936 Cook 1971, Levin 1973 Gödel 1931 Lorenz 1963

Energy only changes its form Available energy is reduced Limits for cooling Parallel processing Lowest temperature Energy limit for computation Location and velocity Energy limit for communication Smallest variance Duration and bandwidth of signals Optical imaging instruments Speed of light in vacuum Unsolvable problems Intractable problems Unprovable theorems Chaotic systems

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TABLE IV. KPIs and requirements in 4G and 5G communications networks

Key performance indicator Basic resources Requirement in 4G Requirement in 5G Peak data rate (bit rate) User experienced data rate Peak spectral efficiency Average spectral efficiency Network energy efficiency Area traffic capacity Delay (latency) Reliability

Time Time Time and frequency Time and frequency Energy Time and space Time Data

1 Gbit/s 10 Mbit/s 15 bit/s/Hz 3 bit/s/Hz 1× 0.1 Mbit/s/m2 10 ms 99.99%

20 Gbit/s 100 Mbit/s 30 bit/s/Hz 9 bit/s/Hz 100× 10 Mbit/s/m2 1 ms 99.999%