SIMULATION DESIGN OF WIRELESS COMMUNICATIONS...

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intRoduction

SIMULATION DESIGN OF WIRELESS COMMUNICATIONS FOR DIGITAL UNIVERSITIES IN DEVELOPING COUNTRIES1

Sonia foRconi, Giuseppe iAzeollA, Alessandra PieRoni, Università degli stUdi “gUglielmo marConi” and tor vergata University, rome, italy2

ABstRAct

Developing countries have lagged behind much of the world in wired communication infrastructures, which are either under-developed or not existing. Suburban, rural and inaccessible areas, in such countries, have made up this gap through the use of wireless communications. The generally available 2nd and 3rd Generation wireless infrastructures, however, do not meet the communication requirements of digital universities, which are not limited to high-speed and broadband connectivity. Digital universities, indeed, generate video streaming and VoIP traffics that require the ability to transmit real-time, interactive video and auditory with low-delay and high-priority. Such features can be met by evolving technologies such as WiMAX and LTE. Among the two, WiMAX better meets the requirements of digital universities in developing countries, being low-cost and easily adaptable to suburban, rural and inaccessible areas. In this paper, a simulation approach is presented to design WiMAX infrastructures for digital universities and optimize their cost and quality of service. The approach is based on Distributed Simulation (DS) and on the use of specific simulation tools (wDSEnv and wDSLang) that together give a complete software suite to overcome the difficulties and the cost of conventional commercial DS.

KEYWORDS: Digital universities, Distance education, Distributed simulation, Simulation, Wireless communication

Developing countries suffer by underdeveloped or non-existing wired communication infrastructures

in suburban, rural and inaccessible areas since (Malindi, 2008) of a series of reasons such as

• Lack or absence of reliable electricity supply, usable water, access roads and regular transport

• Difficult topographical conditions for the presence of lakes, rivers, hills, mountains or desert

1 Received 14 October 2014 - Revised 3 November 2014 2 This work was partially supported by “Guglielmo Marconi” University and by Roma TorVergata – Telecom Italia PhD research program.

SIMULATION DESIGN OF WIRELESS COMMUNICATIONS G.iazeolla et al.

DIGITAL UNIVERSITIES International Best Practices and Applications V.1 - N.158

• Low level of economic activities mainly based on agriculture, fishing, handcrafts, etc.

• Low population density

• Difficult terrains and severe climatic conditions

• High cost of ICT based services

• Lack of or limited access to basic ICT infrastructures.

To overcome difficulties above, such countries have made up the gap through the use of wireless

communication infrastructures, whose construction and setting up is cheaper, faster and more

flexible.

2G 3G 4G

WWAN WWAN WWAN WWAN

GSM UMTS WiMAX LTE

Coverage AreaFrom tens of kilometers to thousands of kilometers

From tens of kilometers to thousands of kilometers

Up to a few tens of KmFrom tens of kilometers to thousands of kilometers

Operating Frequency

850 - 900 MHz 1.8 - 1.9 GHz

850 MHz2.1 GHz

2.6 - 3.5 - 5 GHz 2.6 GHz

Bandwidth 200 KHz 5 MHz Up to 10 MHz1.4, 3, 5, 10, 15, 20 MHz

Data Rate 9.6 kbps 2 Mbps 70 Mbps100 Mbps (Downlink)50 Mbps (Uplink)

Quality of Service (QoS)

Not guaranteed Not guaranteed Guaranteed Guaranteed

Available wireless infrastructures, however, are based on GSM and UMTS technologies3,4 , 2nd

Generation (2G) and 3rd Generation (3G) respectively. Such technologies are able to cover large

geographic areas (so they can be used to implement WWAN networks, i.e. Wireless Wide Area

Networks) and their characteristics are illustrated in Tab. 1, which shows how only the UMTS

among the two, may guarantee high-speed and broadband connectivity which, however, are not

enough to deliver the necessary Quality of Service (QoS) for digital universities.

Indeed, according to International Telecommunication Union Focus Group 7 (ITU-D Focus

Group), digital universities distance education requires features such as:

• Delivery of multimedia contents to remote areas

• Virtual classroom using videoconferencing facilities

• Online academic databases, bibliographic access

3 The 2nd Generation (2G) Global System for Mobile Communication (GSM), developed in the early 1990s, ensures a wide coverage area, transfer data rate of 9.6 kbps, with bandwidth of 200 KHz. This characteristic of GSM worksfor rural wide areas and low population density, but is expensive and inefficient to support modern Internet applications, like web browsing, videoconferencing and multimedia streaming, and does not guarantee Quality of Service (QoS). 4 The Universal Mobile Terrestrial System (UMTS ) belongs to the 3rd Generation (3G) wireless technologies and provides much higher data ratesthan GSM for multimedia services. The installations remain expensive and the standard does not guarantee QoS management.

Table 1. The considered wireless communication infrastructures

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• E-mail and online discussion groups

• Web browsing for teaching and learning materials.

Such features imply low-delays and high-priority in the delivery of video-stream and VoIP traffics

to guarantee real-time, interactive video and auditory. Unfortunately, neither GSM nor UMTS

may guarantee acceptable QoS, as shown in Tab.1, whose prerequisites are low-delays and high

priority in packet delivery.

To support QoS, strategies are to be implemented for the various traffic classes. In other words,

strategies to ensure better services to the upper class categories of users, in terms of high

service availability, high packet throughput, low transmission delay, low jitter, low packet loss,

etc.

Instead, QoS is guaranteed by WiMAX and LTE5 or 4th Generation (4G) technologies.

The former can be used to implement WMAN networks (i.e. Wireless Metropolitan Area Networks),

and the latter WWAN networks, as also shown in Tab. 1.

Between WiMAX and LTE, however, the former is the technology that better meets the requirements

of digital universities in developing countries, being low-cost and easily adaptable to suburban,

rural and inaccessible areas (Malindi et al., 2008; Kumar, 2011; Abhijit, 2007; Sedoyeka, 2012;

Galperin, 2005).

This paper will thus concentrate on WiMAX and will present a simulation paradigm and tool to

design WiMAX infrastructures for digital universities in developing countries and optimize their

cost and quality of service.

Following sections give a description of the WiMAX technology, an overview on the WiMAX cost

and performance versus LTE and 2G/3G, the simulation approach and the WiMAX simulation

tools with a brief illustration of an example use.

the WimAx technology

The IEEE 802.16 Standard for Wireless Metropolitan Area Networks has been commercialized

under the name WiMAX (Worldwide Interoperability for Microwave Access). The standard offers

low-cost and easily adaptable solutions for the “last mile” connection in rural, suburban and

inaccessible areas. With respect to UMTS the standard provides higer data-rates, call admission

control and scheduling service to support the QoS management, as mentioned in Table 1.

For the sake of conciseness, we assume the reader is familiar with the WiMAX protocol and with

physical and MAC layers, as by literature (Andrews et al., 2008).

This paper studies the WiMAX network as consisting of a set of Subscriber Stations (SS), one

Base Station (BS) and a number of Terminal Equipments (TE, e.g.: smartphones, tablets, personal

computers, etc.).

Each SS provides connectivity between a set of TEs and the BS. The BS is responsible for

providing connectivity, radio resource management, and QoS control.

5 The Long TermEvolution (LTE) Standard belongs to the 4th generation (4G) and provides significantly increased data rates (100 Mbps in downlink and 50 Mbps in uplink), reduced latency, extended coverage area, scalable bandwidth capacity, call admission control and schedu-ling service to support the QoS management.



The network architecture can be Point-to-Multipoint (PMP) or Mesh. In the PMP the connections

are between each SS and the BS, which represents the central node responsible for coordinating

all communications and providing connectivity to the various SS. No direct communication is

admitted among the various SSs. In the Mesh architecture, instead, each SS is able to instantiate

direct communication links to any other SS and links towards the associated BS.

Figure 1 gives an example of PMP architecture, where node R represents the interconnection

infrastructure between the Subscriber Stations (SS1 through SS12) and the Base Station.

Each SS is assumed to serve two terminal equipments (TE1 and TE2). The WiMAX standard

contemplates four service categories:

I. Unsolicited Grant Service (UGS): designed for real-time traffic that generates fixed-size

data packets on a periodic basis, such as VoIP

II. Real time Polling Service (rtPS): designed for real-time traffic that generates variable size

data packets on a periodic basis, such as video streaming

III. Non-real time Polling Service (nrtPS): designed for non-real-time service flows that

require variable size data grants on a regular (but not strictly periodic) basis, such as

high File Transfer Protocol (FTP)

IV. Best Effort (BE): designed for traffic where no throughput or delay guarantees are

provided, such as Email and Peer to Peer.

The QoS for such four service categories is obtained by imposing lowest delays and highest

priorities for the UGS service, and highest delays and lowest priorities for the BE. This is obtained

by the call admission control mechanism (CAC) and scheduling algorithms implemented by

the BS and the SS. The call admission control limits the number of admitted connections to

not overload the network in presence of a large number of low priority users. The scheduling

algorithms ensure that the required amount of resources is granted for the admitted connections.

The reader can find in (Iazeolla et al., 2010) the detailed descriptions of the internal architectures of the BS

and SS stations, and the time frame according to which they interact in the exchange of data packets.

Figure 1. Example point-to-multipoint WiMAX architecture

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WimAx cost And PeRfoRmAnce veRsus lte And 2g/3g

As already stated in the Introduction, WiMAX is the technology better suited to the requirements of

Digital Universities in developing countries, being low-cost and easily adaptable. Indeed, literature

shows that WiMAX is not only more convenient than LTE in terms of costs but also with respect to

second generation (GSM) and third generation (UMTS) systems. In fact, (Mishra S. H. et al., 2005;

Lele A. et al., 2007; and Smura T., 2005) prove that the Base Station (BS) cost per user in GSM and

UMTS is at least one order of magnitude larger than in WiMAX (2x104 $ per user against 2x103

$). Literature (Lele A. et al., 2007) also proves that the simulation models (including the ones we

introduce in next sections) can be used to choose the parameters one should give to the BS to

optimize its performance. In particular, simulation can be used to derive the optimal BS buffer-size

to minimize the call blocking probability, the delay jitter per packet and the packet loss rate, in order

to maximize the UGS traffic (number of VoIP calls) and the rtPS traffic (size and frequency of video

streaming packets). Returning to the difference in costs, besides the BS costs one should also

consider the extra cost of the Core site, proper of the GSM and UMTS architectures (see Figure 2).

Such a cost amounts to order 105 $ in GSM and order 106 $ in UMTS (Harno J. et al., 2007), and remains still present in the LTE architecture, where a Core site can also be identified, even though with different functions (see Figure 3).

Such an extra cost is instead absent in WiMAX where the functions of the Core site are embedded in the Base Station.

Finally, there exists a further cost-reason that plays in favour of WiMAX. This is illustrated by (Alebicto M., 2010) who deals with a third component of the costs. In other words, the initial costs of the wireless infrastructures.

Figure 2. 2G/3G overall architecture (Alcatel-Lucent, 2009)

Figure 3. LTE overall architecture (Alcatel-Lucent, 2009)



Such costs are lower for WiMAX with respect to LTE in all geographical sites where pre-existing

2G or 3G infrastructures do not exist (East Asia and Africa have examples of this). Indeed, while

an LTE installation bases itself on the pre-existence of 2G or 3G equipments, WiMAX can do

without that. In other words, WIMAX infrastructures can be installed in the total absence of pre-

existing wireless infrastructures.

oveRvieW of the simulAtion APPRoAch

Many works can be found that deal with the simulation of wireless systems, and WiMAX in particular,

all based on conventional simulation approaches. In this paper new simulation approach is

introduced for WiMAX systems, specifically oriented to the design of distance education systems.

Two simulation paradigms are considered: the “local” and the “distributed” simulation paradigm,

and two software tools are introduced to ease the model development in both paradigms.

A simulation model can be seen as consisting of a set of sub-models or federates. In local

simulation (LS) a single federate exists that simulates the entire system and is run by a single

processor or host. In a distributed simulation (DS), instead, various federates that simulate

distinct parts of the system are run by distinct processors or hosts, connected via a metropolitan

or wide area network.

The DS paradigm is used as a means to achieve one or more of the following goals (Riley et al.,

2004):

I. Scalability, i.e., a facility to scale up for the increased computational requirements of

complex system simulation, by partitioning the system and executing it onto a number

of local processors or geographically distributed hosts;

II. Aggregation and reusability, i.e., a way to use simulation systems available in separate

sites and aggregate them into a more complex system;

III. Parallelism, i.e., exploiting the intrinsic parallelism (where existing) of a given simulation

system by partitioning it into a number of federates run by multiple processors or hosts

(in many cases, the obtained speed-up may largely compensate the communication

overhead of the federates).

The scalability and parallelism properties of the DS paradigm may be useful to deal with

complex systems, such as wireless distance education systems, that consist of a large number

of subsystems. Indeed, by putting together various hosts, one may count on larger amounts of

memory and much lower run-times if the federates are run in parallel.

A further reason why the DS paradigm may turn to be particularly useful is the fact that DS is also

a way to exploit the natural distribution of wireless systems. Indeed, one may decide to locate

the federates in the same geographic locations of the SSs and the BS to be simulated (Iazeolla

et al., DEVS10). This may give several advantages in the design of new networks or the redesign

of existing ones.

A progressive refinement study can indeed be foreseen:

Study step_1) At beginning of the study, an LS version of the wireless system can be produced,

running on a single host, to obtain a first rough evaluation of the system. This version will include

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the simulator of the network infrastructure R. Given the complexity of the various R aspects

to simulate (beaming, frequency and time division multiplexing, multi-carrier modulation,

path loss, shadowing, sectoring, noise, fading), this version, in many cases, includes various

approximations, with a loss of representativeness. If a DS version is instead produced, a more

representative simulator can be obtained, as explained below.

Study step_2) In a second phase, the LS system can be turned into a DS one, with geographically

distributed hosts (i.e., WAN-connected hosts). By using the strategy of positioning the hosts

in the same geographic locations planned for the SSs and the BS, there will be no need of

simulating the network infrastructure R. Indeed, in this case, the real R can be used, and the

DS simulator will only consist of the federates of the SSs and the BS, thus implementing an HIL

(Hardware in-the-loop) simulation (Schafer, 1993; Isermann et al., 1999; Biegeleisen et al., 2005).

The DS simulator will then be run and, in case the simulation results show that the given R is not

satisfactory to meet the QoS requirements for digital universities, iteratively adjustments will be

tried on R, until the QoS requirements are satisfied.

descRiPtion of the WimAx simulAtion tool

The above-illustrated DS simulation approach needs appropriate simulation tools to be

implemented.

The existing commercial tools, e.g.: the HLA-based tools (IEEE std. 1516, 2000; Karlsson et al.,

2001), make the DS approach very expensive with respect to the conventional LS approach, for

a series of reasons better illustrated in the Simulation Tools section. This paper tools are, instead,

very cost-effective and thus they are particularly suited to the study of wireless infrastructure for

digital universities.

The tools consist of a simulation language and a simulation environment, namely:

I. A Java-based simulation language (wDSLang)

II. A DS environment (wDSEnv)

The wDSEnv is a distributed simulation environment that obscures the difficulties of using

conventional-commercial environments such as HLA, and the wDSLang is a Java-based high

level simulation language to easily port LS system into a DS one, as better seen in the Example

use of wDSEnv section.

The advantage of using the wDSLang is the flexibility and algorithmic capability of a general

programming language such as Java, that encompasses all limitations of higher level languages

that can be found in literature, e.g. OpNET (O.T. Incorporated), QualNET (S.N.Technologies),

OMNET++ (OMNeT++ User Guide, 2009), NS-2 (NS-2), etc.

Such languages are also rarely endowed of the primitives necessary to simulate the specific

components of wireless systems, and they make rarely clear which details they model and which

accuracy they obtain in the statistical techniques (Kavin et al., 2002). In addition, in many cases,

they do not accept ones own code to simulate particular algorithms or details.

There also exist the problem of the time such higher-level languages consume to run the



simulations. In NS-2, for example, Link level simulation consumes order weeks of processing

time (Sayenko et al., 2009). System level, consumes order days or even weeks. Packet level

consumes times of order from minutes to days. Our Java-based simulation system, that models

the stations at packet level including the MAC layer and part of the physical layer (frame structure),

consumes instead times of order from seconds to hours.

Besides all above limitations, the mentioned languages and tools, only support the LS paradigm

and their extension to achieve the DS one can be very problematic, as better discussed in the

advancement of wDSEnv and wDSLang with respect to the state of the art section.

The advantage of using wDSEnv is the ease and the economy of use, since, as already said

above, the existing distributed simulation tools make the DS paradigm very expensive and hard

to use. Indeed, as shown in a number of papers (see DAmbrogio et al., 2007 through 2008), using

the existing DS tools, e.g., HLA (Std. 1278.1A, 1998; IEEE std. 1516, 2000; Kuhl et al., 1999)

remains the main obstacle to the wide adoption of the DS paradigm, since of:

• The extra-effort needed to learn how to use the DS standard (such as HLA)

• The extra software to develop for the synchronization and communication between

federates in the DS environment

Which together play against the study of wireless systems for distance education, in force of the

number of stations to simulate.

THE SIMULATION TOOLS

The wDSEnv and wDSLang tools thus together give a complete software suite that makes the

use of distributed simulation of wireless systems effortless and generally accessible.

WDSENV

The distributed simulation environment wDSEnv structures the simulation system according to a

stack of four independent layers (DAmbrogio et al., 2006 through 2009; SimArch Project), each

dealing with a specific issue and each introducing a more abstract set of simulation services

on top of the underlying layers. Layer 4 is the layer where the wireless distributed system is

described in terms of wDSLang primitives. Layer 3 gives the wDSLang primitives. Layer 2 (the

so-called execution container layer) deals with the synchronization and communication of the

simulation entities, transparently for the LS and the DS paradigms. Layer 1 provides all services

needed for the distributed discrete event simulation abstraction on top of the bottom Layer 0

that holds the distributed simulation technology (HLA), made totally invisible to the developer

that operates at Layer 4 level.

WDSLANG

The distributed simulation language wDSLang is a Java-based domain specific language

that offers a set of queueing specific primitives, in addition to the standard facilities of Java

(DAmbrogio et al., 2007; DAmbrogio et al., ISCIS06).

The wDSLang primitives are grouped into various categories: sources, waiting systems, service

centers, links, routers, special nodes (e.g.: ForkNode, JoinNode, PassiveQueue, etc.) and other

system components, called support components (e.g.: User, User generator, Service Request

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generator, Scheduling Policy, etc.).

The wDSLang also includes a special framework called jRand (DAmbrogio et al., EMS2008) for

the definition of random sequences, for the simulation of the system workload.

SIMULATION OF THE WIMAX WORKLOAD

The jRand framework, built in the wDSLang, is invoked each time a random sequence is to be

produced to simulate one of four possible workloads corresponding to the four traffic categories

(UGS, rtPS, nrtPS and BE), mentioned in the WiMAX technology section. More specifically the

jRand framework produces simulation sequences (Forconi et al., 2008) for the following traffic

types:

• VoIP (UGS service)

• Video Streaming (rtPS service)

• FTP (nrtPS service)

• Peer-to-Peer (BE service)

Each traffic type is simulated by a Traffic Model (TM) consisting of a simulation random sequence

characterized by the data packet Interarrival Time (IAT) and the packet size as illustrated in Table 2.

Traffic Type IAT distribution/parameter valuesPacket Size distribution/parameter values

Video StreamingTruncated Pareto α=1.2, K=0.025 sec,

mean=0.006 sec, Maximum=0.0125 secTruncated Pareto α=1.2, K=40 bytes, mean=100 bytes, Maximum=250 bytes

FTP Exponential, mean=40 sec Geometric, mean=20 Kbytes

P2P Bit TorrentTwo-stage hyperexponential p=0.6575, λ1=0.0566, λ2=0.3653

Deterministic, 128 bytes

The traffic simulation sequence for VoIP needs to be characterized both by the IAT and the Packet

Size and by the duration of the ON and OFF voice periods, as illustrated in Table 3.

The complete WiMAX workload model (WML) uses the above traffic models (TMs) and merges

them stochastically by use of a Markov Modulated Bernoulli Process (MMBP), as illustrated in

Figure 4.

Each state in Figure 4 represents the simulation of a specific traffic type (VoIP, Video Streaming,

FTP and P2P). Arrows indicate the switching from a traffic type to another. The figure shows an

example modeling of user interaction by use of its Terminal Equipment (TE, e.g.: a smartphone).

Table 2. WiMAX traffic models for Video Streaming, FTP and P2P



ON period OFF period

DistributionExponential

λON = 1.004 sec

Exponential

λOFF = 1.587 sec

IAT Deterministic, 0.02 sec 0

Packet Size Deterministic, 23 bytes 0

The example actor first uses the TE to perform VoIP for a given time. Then switches to FTP to

download a file from a server, then to Video streaming to see a video, and so on.

The complete definition of the MMBP requires knowledge of the time duration of each state and

the probability transition between states. Such data can be found in (Klemm et al., 2003; Willinger

et al., 1997; Robinson, 2004; Symington, 2008) where the equilibrium probabilities of each state

are also derived. The knowledge of such probabilities combined with the TM of each traffic class

is used by wDSLang to simulate the complete system.

EXAMPLE USE OF WDSENV

As said above, developing a DS system implies distributing various federates among distinct hosts

connected via metropolitan or wide area network. Each federate may simulate a single station e.g.:

one federate for the BS, one federate for the SS1, one federate for the SS2 and so on.

Alternatively, one federate may simulate a set of SSs, or a set of SSs and the BS etc., as seen

later on in Figure 6. The way the various stations are grouped into federates is based on simulation

optimization choices that are not of interest of this paper, which instead only intends to introduce

the reader to the use of the tools to obtain a DS system. In this section we thus proceed to first

develop the LS version of a simple application example and then transform it into its DS form. The

application example assumes the following WiMAX system is to be simulated:

• PMP network architecture

• One BS station and twelve SS stations

• Each SSs serving two TEs

Table 3. WiMAX traffic model for VoIP

Figure 4. Use case of the WiMAX workload

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• Each TE generating streams of all four traffic categories (UGS, rtPS, nrtPS, BE).

For the sake of brevity the wDSLang program of only one SS is presented (all the others being

identical and the BS code being similar).

THE LOCAL SIMULATION EXAMPLE

Figure 5 gives an illustration of the pseudo-code of the LS, in other words, the pseudo-code of the local

simulator run by a single host.

For the sake of simplicity, node R is assumed as a simple delay center. As specified at Step_1 of Overview

of the Simulation approach section, the tool may accept more detailed descriptions of the internal

structure of R, for example details can be introduced to simulate the physics of signal propagation,

wireless channel contention, beaming, frequency and time division multiplexing, multi-carrier modulation,

path loss, shadowing, sectoring, noise, fading, etc., between R and the BS and vice-versa, and between

R and the SSs stations and vice-versa. Similar details can be introduced in the case of mobile system to

simulate, for example, signal propagation between each SS and its connected TEs.

The code first imports the BS, the SS and the R libraries and the Java util libraries. Then instantiates

one object of class BS, twelve objects of class SS and one object of class R. The connection

components (Links) are then declared according to the PMP topology, in other words, one link

between the BS and the wireless infrastructure R and one between R and each SS. The concluding

statement is the program run command. It is sufficient to launch only one component (the BS in this

example) to run the whole simulation model. The command includes three methods:

• getExecutionContainer()

• getUserInterface()

• startEngine()

Where, the first is an invocation to the system procedure called execution local container, the second an

invocation to the procedure that gives the statistics output and the third is the actual starting engine method.

Figure 5. The wDSLang pseudo-code for the LS simulation system



It is obvious that each of the imported libraries implements a particular entity (the BS, the SS or

the R wireless infrastructure entity). The wDSLang code of the SS will consist of the instantiation

of the SS internal components (Iazeolla et al, 2010) that is:

• Transmitter center

• Receiver center

• UL scheduler

• All relative waiting systems

• All relative interconnections links.

The wDSLang declaration of such components is part of the SS.java code. For more details on

the SS java code the reader can refers to (Iazeolla et al., 2010).

According to our assumptions, in addition to these components, it is necessary to instantiate

two data terminal components (TE1 and TE2) for each SS entity, by first importing the basic

wDSLang Source component and then instantiating the relative objects.

The TE declaration includes the parameter declaration to specify that the source is an infinite

source. To complete the system description, the BS and the SSs stations are connected to the

R node by link1 through link13, as shown in Figure 5. Such links are named BasicLinks to be

distinguished from distributed links named DisLinks of the distributed version.

THE DISTRIBUTED SIMULATION EXAMPLE

In this Section, we proof how the above developed LS can be transformed into its equivalent

DS, where the original simulation system is partitioned in three submodels, or federates, each

running on a separate host, as illustrated in Figure 6, with:

• Federate1 consisting of stations BS and SS1 through SS6

• Federate2 consisting of node R

• Federate3 consisting of stations SS7 through SS12.

As specified at Step_2 of Overview of the Simulation approach section, Federate2 may now

consist of the real R, thus implementing, as already said, an HIL (Hardware in-the-loop) simulation

(Schafer, 1993; Isermann et al., 1999; Biegeleisen et al., 2005).

The software code for the distributed simulation system can be derived from the above-defined

LS version with minor adjustments. In particular, the model sections, relating to local items

(either an entity or a link), are declared using the same statements of the LS system. Differently,

the declarations of parts involving references to remote items are to be defined by use of the

distributed versions of the entity and link, called remote entity and remote link.

Determining which items are to be declared by their distributed statement version is immediate

and can be systematically inferred by the model partitioning, which defines the entities running

on each host and the links between submodels.

A very intuitive procedure to carry out such task is to draw the model entities and their connections

and then operate the partitioning by drawing federate borders, as in Figure 6, to separate the

model into submodels.

The dotted arrows that cross the border lines denote remote links and connect a local entity of

a given federate with the remote entity of a different federate, and therefore have to be declared

by their distributed version. For each remote entity, a proxy has to be included locally in the

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definition of the submodel, in order to declare the remote entities that can receive messages from

the submodel, as better shown below. Following this procedure, the DS version of the model

will consist of three federates, each simulating a submodel, in addition to the HLA Federation

Manager and the RTI, which are known components of any HLA-based system, for which the

reader can see (Std 1278.1A 1998; IEEE std. 1516, 2000; Karlsson et al., 2001) for details.

Federate 1 consists of entities BS, SS1, …, SS6. Such entities are local to Federate1 and their

declaration follows the same statements of the LS system. Such statements can therefore be

copied and pasted in the federate code. However, Federate1 also needs to declare the remote

entities (e.g. the R node) that can receive messages from its local entities. This can be declared

by use of the following statement, which is specific of the distributed version:

#1 EntityRef R_Ref = new EntityRef ( “Federate2”, “R”);

The first parameter (“Federate2”) gives the name of the federate in which the remote entity is

running, and the second (“R”) specifies the local name of the entity in Federate2.

Similar considerations hold for all links in Figure 6, which now are to be declared as distributed

links. Let us, for example, consider the link between the BS and R. This will be declared as a

distributed link by the following statements:

#2 DisInPortR_InPort = new DisInPort (“usersInPort”, R_Ref);

#3 BasicLink l = new DisSimpleLink (BS.getUserOutPort(), R_InPort);

Where, statement #2 declares a local reference to the R input port, and statement #3 connects

the BS output port with the R input port, in a very similar format as for the local connections. All

remaining Federate1 distributed links (SS1 to R, …, SS6 to R) are declared in a similar fashion.

Finally, as also shown in Figure 6, Federate3 consists of entities SS7 through SS12. They are

local to Federate3 and their declaration follows the same statements for the LS system. Their

statements can therefore be copied and pasted in the DS code. However, Federate3 also needs

to declare the remote entities (e.g. the R node) that can receive messages from federate3 local

Figure 6. DS version of the example case



entities, this is declared by statements similar to #1, 2 and 3.

In conclusion, the DS version of the simulation system can be easily derived (and also easily

automated) from the original LS version. The largest part of the DS code (including the codes of

the individual BS, SSs and R) is just copied and pasted in the new version. The only statements to

rewrite are the declarations of remote links. Once the distributed simulation system is developed,

it can be started by first activating the RTI software, then activating the Federation Manager and

finally, Federate 1, Federate 2 and Federate 3 in the order. The RTI software and the Federation

Manager have to be properly configured, for example, the IP addresses and the port numbers of

the federates are to be declared in the RTI Ambassadors local to each federate, etc. Such details,

however, are rather technical and not discussed further since they conventional of any HLA based

implementation (Std 1278.1A, 1998; IEEE std. 1516, 2000; Karlsson et al., 2001).

THE ADVANCEMENT OF WDSENV AND WDSLANG WITH RESPECT TO THE STATE OF THE ART

In this Section we give a brief positioning of wDSEnv with respect to existing environments for

distributed simulation, and of wDSLang with respect to the existing simulation languages.

WDSENV AND THE STATE OF THE ART

After the introduction of HLA, several other environments have been introduced to overcome

HLA drawbacks and to face new needs. They are known as PDNS (Riley et al., 2004), DisSimJava

(Page et al., 1997) and DEVS/HLA (Ziegler et al., 1999). The position of wDSEnv, with respect

to the cited environments, can be found in (DAmbrogio et al., 2006 through 2009) since the

differences between wDSEnv and the cited environments remain the same of the SimArch tool.

Besides all cited differences, wDSEnv is provided with a full functional modeling language,

wDSLang, for the description of queueing based systems, and whose differences from the state

of the art are discussed below.

WDSLANG AND THE STATE OF THE ART

Many successful languages and tools exist to simulate complex systems, such as QNAP, JMT,

Extend, OMNET++, etc. However, the service centers necessary to simulate wireless systems are

far from the conventional service centers such languages in many cases provide. Conventional

service centers offer service to waiting jobs whenever a job is present in the queue. On the

contrary the service centers of the wireless stations offer services only during specific time-

frame periods (Iazeolla et al., 2010).

Besides such limitations, the existing languages only support local simulation and extending

them to include distributed simulation can be very problematic. Existing mechanisms to extend

them to DS are based on the use of wrapping techniques, which however can lead to unstable

code and poor simulator maintainability, because the wrappers contain some logic of their own

in addition to the simulator logic (Verbraeck, 2004). On the other hand, existing environments

that support distributed simulation, such as PDNS, DisSimJava, DEVS/HLA (cited above) neither

support queueing modeling nor are easily extensible to support it.

Looking at the specific case of wireless systems (UMTS, WLAN and WiMAX itself) discrete event

simulators also exist, such as OpNET (commercial product) or NS-2 and OMNET++ (open source

products). Such systems, however, share the problem of making difficult, if not impossible, to

partition and execute a local simulation system onto distributed simulation environments, such

71

as HLA. The wDSLang overcomes such difficulties and facilitates the development of distributed

simulation systems based on the HLA standard, as has been shown in the example above.

conclusion

A wireless communication system for digital universities can be considered a complex system

because of the number of components and the complexity of each component. As such, the

computational requirements for their simulation increase considerably with the number of

simulated entities. Distributed Simulation (DS) provides an effective way to scale up for the

increased computational requirements, by use of a larger number of hosts. In contrast to such

advantages, the problem exists of making the DS approach generally accessible, cost-effective

and friendly. Indeed, the difficulty of using the existing DS tools (such as the HLA-based ones)

remains the main obstacle to the wide adoption of the approach. This is since of the considerable

effort needed, in terms of HLA code and knowledge, compared to conventional local simulation

(LS) systems. To overcome such difficulties, the paper has introduced an HLA-transparent

simulation environment and a Java-based simulation language, respectively known as wDSEnv

and wDSLang. The use of such tools avoids the HLA difficulties and gives way to develop the

DS system without any HLA skill and HLA extra coding effort. In particular, by use of the tools, a

Java-based simulation language can be adopted, the skills needed to develop a DS version are

brought down to the standard skills of an LS one, and once an LS system is obtained, bringing

it into DS form can be done with practically no extra effort.



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