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EC 6014 COGNITIVE RADIO

Unit IV

COGNITIVE RADIO ARCHITECTURE

Architecture is a comprehensive, consistent set of design rules by which a specified set of

components achieves a specified set of functions in products and services that evolve

through multiple design points over time. Introduction of fundamental design rules by

which software-defined radio (SDR), sensors, perception, and automated machine

learning (AML) may be integrated to create aware, adaptive, and cognitive radios

(AACRs) is discussed. These SDRs will have better quality of information (QoI) through

capabilities to observe (sense, perceive), orient, plan, decide, act, and learn (the so-called

OOPDAL loop) in radio frequency (RF) and in the user domains. By performing this

integration, transition from adaptive to a demonstrably cognitive radio (CR) is obtained.

There five complementary perspectives of CR architecture (CRA), namely CRA I, CRA

II, CRAIII, CRA IV and CRA V.

CRA I - Functions, Components, and Design Rules

CRA II - The Cognition Cycle

CRA III - The Inference Hierarchy

CRA IV- Architecture Maps

CRA V- Building the CRA on SDR Architectures

CRA I perspective defines six functional components, black boxes to which are first-level

decomposition of AACR functions and among which important interfaces are defined.

One of these boxes is SDR, a proper subset of AACR. One of these boxes performs

cognition via the Self which is a self-referential subsystem that strictly embodies finite

computing.

CRA II perspective examines the flow of inference through a cognition cycle that

arranges the core capabilities of ideal CR (iCR) in temporal sequence for a logical flow

and circadian rhythm for the CRA. The

CRA III perspective examines the related levels of abstraction for AACR to sense

elementary sensory stimuli and to perceive QoI relevant aspects of a scene consisting of

the user in an environment that includes RFsection.

The CRA IV perspective examines the mathematical structure of this architecture,

identifying mappings among topological spaces represented and manipulated to preserve

set-theoretic properties.

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CRA V perspective reviews SDR architecture, sketching an evolutionary path from the

Software Communications Architecture/Software Radio Architecture (SCA/SRA) to the

CRA. The CRA is expressed in Radio eXtensible Markup Language (RXML).

4.1 CRA I: Functions, Components, and Design Rules

The functions of AACR exceed those of SDR. Reformulating the AACR with self as a

peer of its own user where it establishes the need for added functions by which the self

accurately perceives the local scene including the user and autonomously learns to tailor

the information services to the specific user in the current RF and physical scene.

AACR Functional Component Architecture

The SDR components and the related cognitive components of iCR appear in Figure 4.1.

The cognition components describe the SDR in Radio eXtensible Markup Language

(RXML).RXML so that the self can know that it is a radio and that its goal is to achieve

high QoI tailored to its own users. RXML intelligence includes a priori radio background

and user stereotypes as well as knowledge of RF and space–time scenes perceived and

experienced. This includes both structured reasoning with iCR peers and cognitive

wireless networks (CWNs), and ad hoc reasoning with users, all the while learning from

experience.

Fig.4.1 CRA Augments with SDR

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SDR Components

SDRs include a hardware platform with RF access and computational resources, plus at

least one software-defined personality. The SDR Forum has defined its Software

Communication Architecture (SCA) and the Object Management Group (OMG) has

defined its Software Radio Architecture(SRA). These are similar fine-grained

architecture constructs enabling reduced-cost wireless connectivity with next-generation

plug-and-play. These SDR architectures are defined in Unified Modeling Language

(UML) object models, Common Object Request Broker Architecture (CORBA),

Interface Design Language (IDL), and extensible Markup Language (XML) descriptions

of the UML models. The SDR Forum and OMG standards describe the technical details

of SDR both for radio engineering and for an initial level of wireless air interface

(waveform) plug-and-play. The SCA/SRA was sketched in 1996 at the first US

Department of Defense (DoD) inspired modular multifunctional information transfer

system (MMITS) Forum, was developed by the DoD in the 1990s and the architecture is

now in use by the US military. This architecture emphasizes plug-and-play wireless

personalities on computationally capable mobile nodes where network connectivity is

often intermittent at best.

AACR Node Functional Components

A simple CRA includes the functional components are shown in Figure 4.2. A functional

component is a black box to which functions have been allocated, but for which

implementation is not specified. Thus, while the applications component is likely to be

primarily software, the nature of those software components is yet to be determined. User

interface functions, however, may include optimized hardware (e.g., for computing video

flow vectors in real time to assist scene perception).

Fig.4.2 Minimal AACR Node Architecture

At the level of abstraction of this figure 4.2, the components are functional, not physical.

These functional components are as follows:

1. The user sensory perception (SP), which includes haptic, acoustic, and video

sensing and perception functions.

2. The local environment sensors (location, temperature, accelerometer,

compass, etc.).

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3. The system applications (sys apps) media-independent services such as playing

a network game.

4. The SDR functions which include RF sensing and SDR applications.

5. The cognition functions (symbol grounding for system control, planning, and

learning).

6. The local effector functions (speech synthesis, text, graphics, and multimedia

displays).

These functional components are embodied on an iCR platform, a hardware realization of

the six functions. To support the capabilities, these components go beyond SDR in

critical ways. First, the user interface goes well beyond buttons and displays. The

traditional user interface has been partitioned into a substantial user sensory subsystem

and a set of local effectors. The user sensory interface includes buttons (the haptic

interface) and microphones (the audio interface) to include acoustic sensing that is

directional, capable of handling multiple speakers simultaneously, and able to include full

motion video with visual scene perception. In addition, the audio subsystem does not just

encode audio for (possible) transmission; it also parses and interprets the audio from

designated speakers, such as the user, for a high-performance spoken natural language

(NL) interface. Similarly, the text subsystem parses and interprets the language to track

the user’s information states, detecting plans and potential communications and

information needs unobtrusively as the user conducts normal activities. The local

effectors synthesize speech along with traditional text, graphics, and multimedia displays.

Sys apps are those information services that define value for the user.

Design Rules Include Functional Component Interfaces

The six functional components in Tables 4.1(a) and 4.1(b) imply associated functional

interfaces. In architecture, design rules may include a list of the quantities and types of

components as well as the interfaces among those components. This section addresses the

interfaces among the functional components.

The AACR N-squared diagram of Table 4.1(a) characterizes AACR interfaces. These

constitute an initial set of Aware Adaptive Cognitive Radio (AACR) Application

Programming Interface (API)s. In some ways, these APIs augment the established SDR

APIs. This is entirely new and much needed in order for basic AACRs to accommodate

even the basic ideas of the Defense Advanced Research Projects Agency (DARPA)

NeXt-Generation (XG) radio communications program.

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4. 2 Cognition Cycle

Cognitive Radio Architecture (CRA) comprises a set of design rules by which the

cognitive level of information services may be achieved by a specified set of

components in a way that supports the cost-effective evolution of increasingly

capable implementations over time. The cognition subsystem of the architecture

includes an inference hierarchy and the temporal organization and flow of

inferences and control states—the cognition cycle.

Fig. 4.3 a) Simplified Cognitive Cycle

A cognition cycle by which a cognitive radio may interact with the environment is

illustrated in Figure 4.3a. Stimuli enter the cognitive radio as interrupts, dispatched to the

cognition cycle for a response. Such a cognitive radio continually observes the

environment, orients itself, creates plans, decides, and then acts. In addition, machine

learning is structured into these phases. Since the assimilation of knowledge by machine

learning can be computationally intensive, cognitive radio has sleep and prayer epochs

that support machine learning. A sleep epoch is a relatively long period of time (e.g.

minutes to hours) during which the radio will not be in use, but has sufficient electrical

power for processing. During the sleep epoch, the radio can run machine learning

algorithms without detracting from its ability to support its user’s needs. Learning

opportunities not resolved in the sleep epoch can be brought to the attention of the user,

the host network, or a designer during a prayer epoch.

During the wake epoch, the receipt of a new stimulus on any of its sensors initiates a new

primary cognition cycle. The cognitive radio observes its environment by parsing

incoming information streams. These can include the monitoring of radio broadcasts, e.g.

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the weather channel, stock ticker tapes, etc. Any RF-LAN or other short-range wireless

broadcasts that provide environment awareness information are also parsed. In the

observation phase, it also reads location, temperature, and light level sensors, etc. to infer

the user's communications context. The cognitive radio orients itself by determining the

priority associated with the stimuli. A power failure might directly invoke an act

(―Immediate‖ path in the figure). A nonrecoverable loss of signal on a network might

invoke reallocation of resources, e.g. from parsing input to searching for alternative RF

channels. This is accomplished via the path labeled ―Urgent‖ in the figure. However, an

incoming network message would normally be dealt with by generating a plan (Normal

path). Planning includes plan generation. As formal models of causality are embedded

into planning tools, this phase should also include reasoning about causality. The Decide

phase selects among the candidate plans. The radio might have the choice to alert the user

to an incoming message (e.g. behaving like a pager) or to defer the interruption until later

(e.g. behaving like a secretary who is screening calls during an important meeting).

―Acting‖ initiates the selected processes using effector modules.

Learning is a function of observations and decisions. For example, prior and current

internal states may be compared with expectations to learn about the effectiveness of a

communications mode. The cognition cycle implies a large scope of hard research

problems for cognitive radio. Parsing incoming messages requires natural language text

processing. Scanning the user’s voice channels for content that further defines the

communications context requires speech processing. Planning technology offers a wide

range of alternatives in temporal calculus, constraint based scheduling, task planning ,

causality modeling, and the like. Resource allocation includes algebraic methods for

wait-free scheduling protocols, Open Distributed Processing (ODP), and Parallel Virtual

Machines (PVM).

Observe (Sense and Perceive)

The iCR senses and perceives the environment (via ―observation phase‖ code) by

accepting multiple stimuli in many dimensions simultaneously and by binding these

stimuli all together or more typically in subsets to prior experience so that it can

subsequently detect time-sensitive stimuli and ultimately generate plans for action. Thus,

iCR continuously aggregates experience and compares prior aggregates to the current

situation. A CR may aggregate experience by remembering everything.

Orient

The orient phase determines the significance of an observation by binding the observation

to a previously known set of stimuli of a scene. The orient phase contains the internal

data structures that constitute the equivalent of the short-term memory (STM) that people

use to engage in a dialog without necessarily remembering everything with the same

degree of long-term memory (LTM). Typically people need repetition to retain

information over the long term. The natural environment supplies the information

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redundancy needed to instigate transfer from STM to LTM. In the CRA, the transfer from

STM to LTM is mediated by the sleep cycle in which the contents of STM since the last

sleep cycle are analyzed both internally and with respect to existing LTM.

Stimulus Recognition

Stimulus recognition occurs when there is an exact match between a current stimulus and

a prior experience. The CR I prototype is continually recognizing exact matches and

recording the number of exact matches that occurred along with the time measured in the

number of cognition cycles between the last exact match. By default, the response to a

given stimulus is to merely repeat that stimulus to the next layer up the inference

hierarchy for aggregation of the raw stimuli. But if the system has been trained to respond

to a location, a word, an RF condition, a signal on the power bus, or some other

parameter, it may either react immediately or plan a task in reaction to the detected

stimulus. If that reaction were in error, then it may be trained to ignore the stimulus,

given the larger context, which consists of all the stimuli and relevant internal states,

including time.

Binding

Binding occurs when there is a nearly exact match between a current stimulus and a prior

experience and very general criteria for applying the prior experience to the current

situation are met. One such criterion is the number of unmatched features of the current

scene. If only one feature is unmatched and the scene occurs at a high level such as the

phrase or dialog level of the inference hierarchy, then binding is the first step in

generating a plan for behaving in the given state similar to the last occurrence of the

stimuli.

Plan

Most stimuli are dealt with deliberatively rather than reactively. An incoming network

message would normally be dealt with by generating a plan (in the plan phase, the normal

path). Such planning includes plan generation. In research quality or industrial-strength

CRs, formal models of causality must be embedded into planning tools. The plan phase

should also include reasoning about time.

Decide

The decide phase selects among the candidate plans. The radio might have the choice to

alert the user to an incoming message (e.g., behave like a pager) or to defer the

interruption until later (e.g., behave like a secretary who is screening calls during an

important meeting).

Act

Acting initiates the selected processes using effector modules. Effectors may access the

external world or the CR’s internal states.

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Externally Oriented Actions

Access to the external world consists primarily of composing messages to be spoken into

the local environment or expressed in text form locally or to another CR or CN using the

Knowledge Query and Manipulation Language (KQML), Radio Knowledge

Representation Language (RKRL), Web Ontology Language (OWL), Radio eXtensible

Markup Language (RXML), or some other appropriate knowledge interchange standard.

Internally Oriented Actions

Actions on internal states include controlling machine-controllable resources such as

radio channels. The CR can also affect the contents of existing internal models, such as

adding a model of stimulus experience response to an existing internal model structure.

The new concept itself may assert-related concepts into the scene. Multiple independent

sources of the same concept in a scene reinforce that concept for that scene. These

models may be asserted by the self to encapsulate experience. The experience may be

reactively integrated into RXML knowledge structures as well, provided the reactive

response encodes them properly.

Learning

Learning is a function of perception, observations, decisions, and actions. Initial learning

is mediated by the observe phase perception hierarchy in which all SP are continuously

matched against all prior stimuli to continually count occurrences and to remember time

since the last occurrence of the stimuli from primitives to aggregates. Learning also

occurs through the introduction of new internal models in response to existing models

and case-based reasoning (CBR) bindings. In general, there are many opportunities to

integrate ML into AACR.

Self-monitoring

Each of the prior phases must consist of computational structures for which the execution

time may be computed in advance. In addition, each phase must restrict its computations

to not consume more resources than the precomputed upper bound. Therefore, the

architecture has some prohibitions and some data set requirements needed to obtain an

acceptable degree of stability of behavior for CRs as self-referential self-modifying

systems.

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Fig. 4.3 b) Simplified Cognitive Cycle

4. 3 CRA III Inference Hierarchy

The phases of inference from observation to action show the flow of inference, a top-

down view of how cognition is implemented algorithmically. The inference hierarchy is

the part of the algorithm architecture that organizes the data structures. Inference

hierarchies have been in use since Hearsay II in the 1970s proposed it, but the CR

hierarchy is unique in its method of integrating markup language (ML) with real-time

performance during the wake epochs. An illustrative inference hierarchy includes layers

from atomic stimuli at the bottom to information clusters that define action

contexts, as shown in Figure 4.4.

Fig. 4.4 Standard Inference hierarchy

The pattern of accumulating elements into sequences begins at the bottom of the

hierarchy. Atomic stimuli originate in the external environment including RF, acoustic,

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image, and location domains, among others. The atomic symbols extracted from them are

the most primitive symbolic units in the domain. In speech, the most primitive elements

are the phonemes. In the exchange of textual data (e.g., in e-mail), the symbols are the

typed characters. In images, the atomic symbols may be the individual picture elements

(pixels) or they may be small groups of pixels with similar hue, intensity, texture, and so

forth.

A related set of atomic symbols forms a primitive sequence. Words in text, tokens from a

speech ―tokenizer,‖ and objects in images (or individual image regions in a video flow)

are primitive sequences. Primitive sequences have spatial and/or temporal coincidence,

standing out against the background (or noise), but there may be no particular meaning in

that pattern of coincidence. Basic sequences, in contrast, are space–time–spectrum

sequences that entail the communication of discrete messages.

These discrete messages (e.g., phrases) are typically defined with respect to an ontology

of the primitive sequences (e.g., definitions of words). Sequences cluster together

because of shared properties. For example, phrases that include words such as ―hit,‖

―pitch,‖ ―ball,‖ and ―out‖ may be associated with a discussion of a baseball game.

Knowledge Discovery in Databases (KDD) and the Semantic Web offer approaches for

defining, or inferring, the presence of such clusters from primitive and basic sequences.

A scene is a context cluster, a multidimensional space–time–frequency association, such

as a discussion of a baseball game in the living room on a Sunday afternoon. Such

clusters may be inferred from unsupervised ML (e.g., using statistical methods or

nonlinear approaches such as Support Vector Machines (SVMs).

NL in CRA Inference Hierarchy

The issues required to integrate existing Natural Language Processing (NLP) tools, the

discussion does not pretend to present a complete solution to this problem of Inference

hierarchy.

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Fig. 4.5 NL in Inference Hierarchy

NLP systems work well on well-structured speech and text, such as the prepared text of a

news anchor. But they do not yet work well on noisy, nongrammatical data structures

encountered, for example, when a user is trying to order a cab in a crowded bar. Thus,

less-linguistic or meta-linguistic data structures may be needed to integrate core CR

reasoning with speech and/or text-processing frontends. The CRA has the flexibility

illustrated in Figure 4.5 for the subsequent integration of evolved NLP tools. The

emphasis of this version of the CRA is a structure of sets and maps required to create a

viable CRA. Although introducing the issues required to integrate existing NLP tools, the

discussion does not pretend to present a complete solution to this problem.

4.4 CRA IV: Architecture Maps

Cognition functions are implemented via cognition elements consisting of data structures,

processes, and flows, which may be modeled as topological maps over the abstract

domains identified in Figure 4.6.

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Fig. 4.6 Architecture based on Cognition Cycle

The Self is an entity in the world, whereas the internal organization of the Self is an

abstraction that models the Self itself.

The hierarchy of words, phrases, and dialogs from sensory data to scenes is not

inconsistent with visual perception. Words correspond to visual entities; phrases to

detectable movement and juxtaposition of entities in a scene. Dialogs correspond to a

coherent sequence of movement within the scope of a scene, such as walking across the

room. Occlusion may be thought of as a dialog in which the room asserts itself in part of

the scene while observable walking corresponds to assertion of the object. The model

data structures may be read as generalized words, phrases, dialogs, and scenes that may

be acoustic, visual, or perceived in other sensory domains (e.g., infrared). These

structures refer to set-theoretic spaces consisting of a set X and a family of subsets Ox

that contain {X} and { }, the null set, and that are closed under union and countable

intersection. In other words, each is a topological space induced over the domain.

Proceeding up the hierarchy, the scope of the space (X, Ox) increases. A Scene is a

subset of space–time that is circumscribed by the entity by sensory limits. The cognition

functions modeled in these spaces are topology-preserving maps as given in Figure 4.6.

Data- and knowledge-storage spaces are shown as rectangles (e.g., dialog states, plans),

whereas processing elements that transform sets are modeled as homeomorphisms, or

topology-preserving maps, shown as directed graphs (e.g., π) in this figure.

CRA Topological Maps

The processing elements of the architecture are modeled topological maps, as shown in

Figure 4.6:

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Behaviors in the CRA

CRA entails three modes of behavior: waking, sleeping, and praying. Behavior that lasts

for a specific time interval is called a behavioral epoch. The axiomatic relationships

among these behaviors are expressed in the topological maps of Figure 4.7

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Fig. 4.7 Cognitive Behavior Model consists of Domains and Topological maps

Waking Behavior

Waking behavior is optimized for real-time interaction with the user, isochronous control

of Software Radio ( SWR) assets, and real-time sensing of the environment. The conduct

of the waking behavior is informally referred to as the awake-state, although it is not a

specific system state, but a set of behaviors. Thus, referring to Figure 4.7, the awake-state

cognition-actions α map the environment interactions to the current stimulus–response

cases. These cases are the dynamic subset of the embedded Stimulus Experience

Response Model (serModel). Incremental ML δ maps these interactions to integrated

knowledge, the persistent subset of the serModels.

Sleeping and Dreaming Behaviors

Cognitive PDAs (CPDAs) detect conditions that permit or require sleep and dreaming.

For example, if the PDA predicts or becomes aware of a long epoch of low utilization

(such as overnight hours), then the CPDA may autonomously initiate sleeping behavior.

Sleep occurs during planned inactivity, for example, to recharge batteries. Dreaming

behavior employs energy to retrospectively examine experience since the last period of

sleep. In the CRA, all sleep includes dreaming. In some situations, the CPDA may

request permission to enter sleeping/dreaming behavior from the user (e.g., if predefined

limits of aggregate experience are reached). Regular sleeping/dreaming limits the

combinatorial explosion of the process of assimilating aggregated experience into the

serModels needed for realtime behavior during the waking behaviors. During the

dreaming epochs, the CPDA processes experiences from the waking behavior using

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nonincremental ML algorithms. These algorithms map current cases and new knowledge

into integrated knowledge β.

A conflict is a context in which the user overrode a CPDA decision about which the PDA

had little or no uncertainty. Map β may resolve the conflict. If not, it will place the

conflict on a list of unresolved conflicts (map γ).

Prayer Behavior

Attempts to resolve unresolved conflicts via the mediation of the PDA’s home network

may be called prayer behavior, referring the issue to a completely trusted source with

substantially superior capabilities. The unresolved-conflicts list γ is mapped ( λ) to

RXML queries to the PDA’s home CN expressed in XML, OWL, KQML, RKRL,

RXML, or a mix of declared knowledge types. Successful resolution maps network

responses to integrated knowledge ( µ). Many research issues surround the successful

download of such knowledge, including the set of support for referents in the unresolved-

conflicts lists and the updating of knowledge in the CPDA needed for full assimilation of

the new knowledge or procedural fix to the unresolved conflict. The prayer behavior may

not be reducible to finite-resource introspection, and thus may be susceptible to the

partialness of Turing Capable (TC), even though the CPDA and CWN enforce watchdog

timers

4.5 CRA V: Building the CRA on SDR Architectures

A CR is an SWR or SDR with flexible formal semantics-based entity-to-entity messaging

via RXML and integrated ML of the self, the user, the RF environment, and the situation.

This presents how SWR, SDR, and the SCA, or SRA, as they relate to the SRA.

Although it is not necessary for an Aware Adaptive Cognitive Radio (AACR) to use the

SCA/SRA as its internal model of itself, it certainly must have some model, or it will be

incapable of reasoning about its own internal structure and adapting or modifying its

radio functionality autonomously.

Review of SWR and SDR Principles

Hardware-defined radios such as the typical amplitude/frequency modulation (AM/FM)

broadcast receiver convert radio to audio using such radio hardware as antennas, filters,

analog demodulators, and the like. SWR is the ideal digital radio in which the analog-to-

digital converter (ADC) and digital-to-analog converter (DAC) convert digital signals to

and from RF directly, and all RF channel modulation, demodulation, frequency

translation, and filtering are accomplished digitally. For example, modulation may be

accomplished digitally by multiplying sine and cosine components of a digitally sampled

audio signal (called the ―baseband‖ signal, to be transmitted) by the sampled digital

values of a higher-frequency sine wave to upconvert it, ultimately to the RF spectrum.

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Fig.4.8 SDR Principle applied to Cellular Base Station

Figure 4.8 shows how SDR principles apply to a cellular radio-base station. The ideal

Software Radio (SWR) would have essentially no RF conversion, just ADC/DAC blocks

accessing the full RF spectrum available to the (wideband) antenna elements. Today’s

SDR-base stations approach this ideal by digital access (DAC and ADC) to a band of

spectrum allocations, such as 75 MHz allocated to uplink and downlink frequencies for

3G services. In this architecture, RF conversion can be a substantial system component,

sometimes 60 percent of the cost of the hardware, and not amenable to cost

improvements through Moore’s law. The ideal SDR would access more like 2.5 GHz

from, say 30 MHz to around 2.5 GHz, supporting all kinds of services in TV bands,

police bands, air traffic control bands, and other bands. Although this concept was

considered radical when introduced in 1991 and popularized in 1995.

Fig.4.9 SWR Principle ADC and DAC at the Antenna

This ideal SWR may not be practical or affordable, so it is important for the radio

engineer to understand the trade-offs. In particular, the physics of RF devices (e.g.,

antennas, inductors, filters) makes it easier to synthesize narrowband RF and intervening

analog RF conversion and intermediate frequency (IF) conversion. Given narrowband

RF, the hardware-defined radio might employ baseband (e.g., voice frequency) ADC,

DAC, and digital signal processing. The programmable digital radios (PDRs) of the

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1980s and 1990s used this approach. Historically, this approach has not been as

expensive as wideband RF (i.e., the cost of antennas, conversion), ADCs, and DACs.

Handsets are less amenable to SWR principles than the base station as in Figure 4.9. Base

stations access the power grid. Thus, the fact that wideband ADCs, DACs, and DSP

(digital signal processor) consume many watts of power is not a major design driver.

Conservation of battery life, however, is a major design driver in the handset.

Thus, insertion of SWR technology into handsets has been relatively slow. Instead, the

major handset manufacturers include multiple single-band RF chip sets into a given

handset. This has been called the Velcro radio or slice radio. The ideal SWR is not

readily approached in many cases, so the SDR has comprised a sequence of practical

steps from the baseband DSP of the 1990s toward the ideal SWR. As the economics of

Moore’s law and of increasingly wideband RF and IF devices allow, implementations

move upward and to the right in the SDR design space as in Figure 4.10.

Fig.4.10 SDR Design Space

This space consists of the combination of digital access bandwidth and programmability.

Access bandwidth consists of ADC/DAC sampling rates converted by the Nyquist

criterion13 and practice into effective bandwidth. Programmability of the digital

subsystems is defined by the ease with which logic and interconnect may be changed

after deployment. Application-specific integrated circuits (ASICs) cannot be changed at

all, so the functions are ―dedicated‖ in silicon. Field-programmable gate arrays (FPGAs)

can be changed in the field, but if the new function exceeds some performance parameter

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of the chip, which is not uncommon, then one must upgrade the hardware to change the

function, just like with ASICs.

The SDR Forum defined a very simple, helpful model of radio in 1997, which is shown

in Figure 4.11. This model highlights the relationships among radio functions at a tutorial

level. The CR has to ―know‖ about these functions, so this model is a good start because

it shows both the relationships among the functions and the typical flow of signal

transformations from analog RF to analog or (with SDR) digital modems, and on to other

digital processing, including system control of which the user interface is a part.

Fig. 4.11 SDR Forum

Software Communications Architecture (SCA)

The US DoD developed the SCA for its Joint Tactical Radio System (JTRS) family of

radios. The SCA identifies the components and interfaces shown in Figure 4.12 The

APIs define access to the PHY layer, to the MAC layer, to the logical link control (LLC)

layer, to security features, and to the input/output of the physical radio device. The

physical components consist of antennas and RF conversion hardware that are mostly

analog and that typically lack the ability to declare or describe themselves to the system.

Most other SCA-compliant components are capable of describing themselves to the

system to enable and facilitate plug-andplay among hardware and software components.

In addition, the SCA embraces the portable operating system interface (POSIX) and

CORBA.

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Fig. 4.12 JTRS SCA Version

The model evolved through several stages of work in the SDR Forum and OMG into a

UML-based object-oriented model of SDR (Figure 14.19). Waveforms are collections of

load modules that provide wireless services, so from a radio designer’s perspective, the

waveform is the key application in a radio. From a user’s perspective of a wireless PDA

(WPDA), the radio waveform is just a means to an end, and the user doesn’t want to

know or to have to care about waveforms. Today, the cellular service providers hide this

detail to some degree, but consumers sometimes know the difference between CDMA

and GSM, for example, because first generation CDMA works in the United States, but

not in Europe. With the deployment of the 3G of cellular technology, the amount of

technical jargon consumers will need to know is increasing. So the CRA insulates the

user from those details, unless the user really wants to know.

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Fig. 4. 13 SDR Forum UML model of radio services

SDR Forum of UML Radio Services

In the UML model shown in Figure 4.13, Amp refers to amplification services, RF refers

to RF conversion, interference management refers to both avoiding interference and

filtering it out of one’s band of operation. In addition, the jargon for US military radios is

that the ―red‖ side contains the user’s secret information, but when it is encrypted it

becomes ―black,‖ or protected, so it can be transmitted. Black processing occurs between

the antenna and the decryption process. The Figure 4.13 has no user interface. The UML

model contains a sophisticated set of management facilities, to which the human–

machine interface (HMI) or user interface is closely related.