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DOI: 10.1177/0037549705049811

2005 81: 241SIMULATIONGavin Yeung, Mineo Takai and Rajive Bagrodia

Effects of Detailed OFDM Modeling in Network Simulation

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Effects of Detailed OFDM Modelingin Network Simulation

Gavin YeungMineo Takai

Rajive BagrodiaComputer Science DepartmentUniversity of California, Los AngelesLos Angeles, CA 90095[gavin,mineo,rajive]@cs.ucla.edu

In mobile ad hoc network (MANET) studies, it is imperative to use highly detailed device mod-els as they provide high-layer protocols with good prediction of underlying wireless communicationperformance. However, such studies often use abstract models for execution speed and simplicity.This article first shows that physical layer variables, including path loss, shadowing, multipath, and

Doppler, have significant effects on thepredicted overall networking performance. It then proposes anapproach to simulate details of wireless propagation and radio characteristics in networking studieswhile still maintaining a reasonable simulation execution time. Through runtime performance studieswith detailed orthogonal frequency division multiplexing (OFDM) Simulink/MATLAB models and theQualNet network simulator, it is shown that the proposed approach can improve the simulation run-time performance by three to four orders of magnitude without compromising the fidelity of simulationresults.

Keywords: Computer networks, network simulation, radio communication, system modeling

1. Introduction

Network simulation is commonly used for the evaluation

of wireless network protocols. Discrete event simulatorstypically model the network activities on a packet- by-packet basis, in time granularity of tens of microseconds,and include a model for each layer of the entire protocolstack.Abstract modelscanbe acceptable if they do not sig-nificantly compromise accuracy of the simulation results.However, even if abstract models may compromise the ac-curacy, they are often in place because detailed models aretoo difficult to implement and run efficiently.

Studies on physical layer techniques and their perfor-mance evaluation under varying channel conditions of-ten use highly specialized mathematical tools such asMATLAB, Simulink, Maple, and Mathematica (e.g., seehttp://www.mathworks.com, http://www.maplesoft.com,

and http://www.wolfram.com). These software packagesprovide a rich set of built-in libraries and standard build-ing blocks for use in the rapid development of prototypes,allowing users to model channel, modulation, and demod-ulation withdifferent parameters. However, this highly de-tailed simulation of receiving every bit transferred acrossthe wireless channel comes at a high computing cost anda very long execution time.

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SIMULATION, Vol. 81, Issue 4, April 2005 241-2532005 The Society for Modeling and Simulation International

DOI: 10.1177/0037549705049811

An abstract model may effectively replace a detailedmodel if such a model does not produce inaccurate re-sults. Such an example would be the recently proposedfluid-based analytical model to determine queue sizes forhigh-capacity wired networks [1, 2]. In other cases, de-tailed simulation models may be necessary to accuratelypredict networkperformance.This is especially truefor thephysical layer in wireless networks, in which slight inac-curacy may become magnified by higher layer protocols.Takai, Martin, andBagrodia [3] show that consideration ofthe physical layer is necessary to determine ad hoc routingnetwork performance. However, even with very strong ev-idence at hand, current network simulators apply abstractmodels to simulate the propagation layer and radio devicecharacteristics. They favor abstract, simple models for thesake of execution speed and efficiency.

There is significant information to be gained in thedetailed simulation of the physical layer, however. In awireless medium in which the channel condition changesfrequently, nanosecond time granularity simulation ofcommunication devices, together with the propagationmedium, provides valuable insights that otherwise wouldbe lost in abstract modeling. This article presents anapproach to develop an appropriate interface betweena packet-level simulator, QualNet (http://www.scalable-networks.com), and a MATLAB/Simulink model for anorthogonal frequency division multiplexing (OFDM) ra-dio and associated channeltwo simulators of dramati-cally different time scales and execution speed.

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Yeung, Takai, and Bagrodia

Our integrationof a highly detailedphysical layer modeland a packet-level simulator demonstrates that detailedsimulation of the physical layer significantly affects theperformance prediction of higher layer protocols. Specifi-cally, it is shown that thenumber of medium-access control(MAC) retransmissions may significantly differ when the

abstract model and the detailed model are compared atvarious data rates. This, in turn, causes a varying degree ofimpact on the packet delivery ratio.

The rest of the article is organized as follows. Section 2gives a survey of related works. Section 3 briefly describesOFDM and the IEEE 802.11 MAC, followed by a dis-cussion of the OFDM model and the network simulator,QualNet. Section 4 presents the integration technique ofthe OFDM model into QualNet. Simulation experimentsand results are presented in section 5. Section 6 followswith our conclusion.

2. Related Work

Previousattempts to integrate heterogeneoussimulators in-clude studies byBucket al. [4],Riley etal. [5],Xuetal. [6],and Zhou et al. [7]. In Buck et al. [4], the Ptolemy projectstudies heterogeneous modeling, simulation, and design ofconcurrent embedded systems. The idea is to use a hetero-geneous software environment to develop heterogeneousdesigns. The cosimulation architecture allows designersfull-system feedback on their design choices, and Ptolemyhas been used for a broad range of applications, includ-ing telecommunications, network design, real-time sys-tems, and hardware/software codesign. Our method of in-tegrating heterogeneous models to develop heterogeneousdesigns is applied to the network simulation domain to re-alize the innovationsthatarebeing madeon different layers

of the network stack.Riley et al. [5] developed a backplane that enables the

user to bring multiple network simulators together andharness their models in a single experiment. By bridgingmultipleheterogeneous network simulators, the backplaneprovides users with the ability to take advantage of thestrength and capabilities of different simulators. The sim-ulation engine exchanges meaningful event messages withother simulators, even when they do not share a common-event message format. The split-protocol stack methodol-ogyfor network simulation presentedin Xu et al.[6] allowsnetwork researchers to run different layers of the networkstack on different simulators. The integration detailed anarchitecture in which multiple simulators are operating at

different levels of fidelity in different networking layers ina single experiment. In Zhou et al. [7], the MAYA mul-tiparadigm, multiresolution, scalable, and extensible net-work modeling framework is used to emulate a distributedmultimedia application. A combination of discrete eventsimulation, analytical modeling, and physicalnetwork em-ulation is tied together to form a heterogeneous modelingparadigm. The goal of MAYA is to study the trade-offsbetween the speed and accuracy of multiple modeling ap-

proaches as a function of different types and scales of net-works, protocols, traffic andapplication types, andmetrics.

The focus of these pieces of work differs from the onedescribed in this study, in that we concentrate on the prob-lems associated with tying a physical radio simulator witha network simulatorsimulators of dramatically different

time granularity and fidelity. The integration of an OFDMmodel into a network simulatoris thefirst nanosecond timestep framework used for network systems study. In partic-ular, we present a method of modeling OFDM characteris-tics in a network simulation environment where trade-offsforspeed instead of complete accuracyareneeded forscal-able network simulation.

3. OFDM and IEEE 802.11 Overview,OFDM Model,and QualNet Network Simulator

3.1 OFDM Technology and IEEE 802.11

To understand the necessity of the integration effort, we

briefly describe the IEEE 802.11a MAC and the OFDMphysical (PHY) layer. Readers interested in the details ofthestandard shouldrefer toIEEE[8,9],VanNee andPrasad[10], and Terry and Heiskala [11]. The IEEE 802.11a usesOFDM as its underlying radio technology. A combinationof different modulation andcodingschemes is used to givethe IEEE 802.11a the wealth of data rates. Operating at the5-GHz band, it supports rates of 6, 9, 12, 18, 24, 36, 48,and 54 Mbps.

OFDM is a modulation scheme that converts a wide-bandsignal intoa series of independent narrowbandsignalsplaced side-by-side in the frequency domain. Modulationis the process of translating an outgoing data stream intosymbols for transmission by the sender. The main benefit

of OFDM is that the subcarriers can actually overlap oneanother. The basic idea is to split the data to be transmittedinto n parallel data streams, each of which is modulatedfor a subcarrier. The entire allocated channel is occupiedby the sum of the narrow orthogonal subbands. Due to im-plementation complexity, OFDM applications have beenscarce until recently, with the advances in Digital SignalProcessing (DSP) technology.

OFDM communication systems naturally alleviate theproblems of multipath propagation, with its low data ratepersubcarrier, as it isonly a fractionofconventional single-carrier systems having the samethroughput. Orthogonalityamong the carriers is achieved by separating the carriersby an integer multiple of the inverse of symbol duration of

the parallel bit stream. However, when the transmitter orreceiver is moving relative to one another, Doppler shiftsoccur and can cause significant problems in OFDM sys-tems as the transmission technique is inherently sensitiveto carrier frequency offsets. Pilot tones are often used forchannel estimation refinement. In the IEEE 802.11a, 4 ofthe 52 subcarriers are designated as pilot tones for correct-ingresidual frequency offset errors that tend to accumulateover symbols. The PHY layer also prepends the physical

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EFFECTS OF DETAILED OFDM MODELING IN NETWORK SIMULATION

SIFSDIFS SIFS SIFS

Busy RTS DATABackoff

CTS ACK

Figure 1. Timing sequence for a request-to-send/clear-to-send (RTS/CTS) exchange

preamble to the data frame, modulates, and codes the dataframe at theMAC-specified data rate. Thephysical pream-ble is used to allow the receiver to detect the start of packettransmission and to synchronize to the transmitters clock.

The IEEE 802.11 MAC is primarily responsiblefor avoiding collisions due to simultaneous transmis-sions by carrier sense multiple-access/collision avoidance(CSMA/CA). Upon detecting a transmission by a neigh-boringnode,a node sets a network allocation vector (NAV)

to yield the channel to the neighbor, avoiding the po-tential collision. Optionally, another medium reservationmechanism is implemented with the request-to-send/clear-to-send (RTS/CTS) [12] message exchange. Nodes thatoverhear an RTS or a CTS do not transmit data until thecorresponding NAV expires. This can alleviate the hid-den terminal problem and thus is typically used in mobilead hoc networks (MANETs). The timing sequence for theRTS/CTS exchange is shown in Figure 1.

By integrating an OFDM model in MATLAB with theIEEE 802.11 MAC protocol implemented in QualNet, wecan now predict the performance of OFDM radio technol-ogy in CSMA/CA networks with details of the physicallayer and device characteristics.

3.2 OFDM Simulator

An OFDM simulator is built using MATLAB/Simulink[13]. Simulinkis a simulationandprototypingenvironmentfor modeling dynamic systems (http://www.mathworks.com/products/simulink). The OFDM simulator contains alarge set of parameters that lead to a myriad of channelconditions and, hence, bit error rates (BERs). The relevantvariable parameters for the purpose of this study includethe following:

Modulation typeBPSK, 4-QAM, 16-QAM, 64-QAM

Multipathup to six channel tap delays and loss

Number of effective subcarriers33 to 1024 subcarriers Number of symbols in cyclic prefix and cyclic postfix

Transmitter antenna gain, receiver antenna gain

Mean transmit power, receiver noise figure

Signal to interference and noise ratio (SINR)

Frequency offset

Theseparameters describing channel characteristics arefirst fed into MATLAB. A channel is then realized in

Simulink. A picture of the OFDM simulator is shown inFigure 2.

The transmitter model in Simulink, upon the start ofsimulation, generates a stream of bits and modulates themby the specified modulation scheme. A random bit gener-ator is used to provide the input stimulus for the system

instead of having data feed in from a MAC layer. Pilottones areadded,andthe last OFDM symbol is zero-paddedprior to the Inverse Fast Fourier Transform (IFFT). Guardblocks areaddedby cyclically prepending andpostpendingthe specified number of data samples to the beginning andendof each individual OFDM symbol. Each data symbol is4.0 s long. A preamble is then generated, which consistsof training symbols for packet detection, frequency offset,and channel estimation at the receiver.

The symbols are then brought to the transmitter radiofrequencyfrontendandsimulated across thewirelesschan-nel. In wireless communication, signals are subjected todistortions generated by the signals interactions with ob-stacles and terrain conditions. Under the assumptions of

multiple propagation paths to the receiver, the channel ischaracterized by time-varying propagation delays, attenu-ation factors, and Doppler shifts.

On the receiver side, the receiver must decide whichof the possible digital waveforms most closely resemblesthe received signal, taking into account the effects of thechannel.TheOFDMreceiversynchronizes to the incomingsignal, and the baseband processor demodulates the signalback to the stream of bits. The receiver first performs timesynchronization and removal of cyclic prefix and postfix.After a fast Fourier transform (FFT), pilot tones are re-moved from thedata frame,andthedata are then reorderedback to the original unscrambled sequence. The transmit-ted and receive bits are compared, and BER is calculatedbased on the number of error bits and the total numberof bits sent. Furthermore, the receiver calculates the SINRper OFDM subcarrier seen at the receiver baseband. Theaverage received effective SINR is calculated at the end ofsimulation. Simulation of 100 OFDM symbols takes about50 seconds on a 2.4-GHz Intel Xeon machine equippedwith 512 MB of memory.

3.3 QualNet Simulator

QualNet is a discrete event network simulator that in-cludes a rich set of detailed models for wireless network-ing. QualNet is the next generation of the GloMoSim sim-ulator [14]. GloMoSim was designed to simulate large-

scale wireless networks with thousands of mobile nodes,each of which may have different communication capa-bilities. QualNet has extended GloMoSims capabilities tosimulate wired networks as well as mixed wired and wire-lessnetworks. QualNet defines simpleapplication programinterfaces (APIs) between neighboring layers to enhancemodular composition of protocol models developed at dif-ferent layers by different designers. The APIs are kept asclose as possible to the operational protocol stack, such

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Figure 2. Graphical user interface (GUI) of the orthogonal frequency division multiplexing (OFDM) simulator

that even operational code is easily integrated into Qual-

Net with this layered design.QualNet includes models of popular protocols used in

each network layer. These range from commonly used ap-plications, such as file transfer (ftp) and Web browsing(http), to transport and MAC layer protocols. QualNet de-fines simpleAPIs between neighboring layers for themod-ular composition of protocol models developed at the dif-ferent layers by different designers. A number of statisti-cal metrics at each layer are collected automatically by thesimulator and can be subsequently used by the analysts toanalyze the experiment results. QualNet implements theIEEE 802.11a MAC and PHY reference standard. Whilethe MAC layer is simulated inside the simulator, the PHYlayer is abstracted to a BER-based signal reception model.

The BER versus SNR performance tables were gener-ated using the OFDM simulator from Terry and Heiskala[11]. The tables were created by running theOFDM modeland statistically generating the results over a number oftrial runs at a specified modulation and coding rate. Theabstract PHYmodel takes theSNRcalculated by theQual-Netchannelmodel andlooksup thecorrespondingBER forthat data rate. It then probabilistically determines whethereach node receives a frame without errors. The error prob-

ability is then calculated using (1), where numBits is the

number of bits simulated for the particular BER.

errorProbability = 1 (1 BER)numBits. (1)

A uniformly distributed random number is then generatedin QualNet. If the error probability is greater than the gen-erated random number, that packet is presumed to have anerror, and the nodes radio unlocks on the signal reception;the signal becomes noise.

4. Integration of OFDM Model into QualNet

This section discusses implementation issues with the in-tegration of the OFDM model with QualNet. To interfacethese two simulators, the time scale and execution speed

differences must be carefully considered. As QualNet isdeveloped usinga layered approach,we canmodify theim-plementation details at a particular layer without affectingother layers. To integrate the OFDM model, the physicallayer in QualNet was modified to invoke theOFDM modelwhen necessary, as shown in Figure 3.

When a QualNet node detects an incoming signal, itfirst determines if that signal is above the receiving thresh-old (RXT). If the signal is above the specified RXT value,




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ApplicationCBR

TransportUDPQualN

et

IPIPv4

NetworkAODV

Link LayerPacket Store/Forward

MAC LayerIEEE 802.11

PhyLayer

SINRINSINR BER Loss

Transmitter ReceiverChannelSINR

OUT

Cache Tables OFDM Simulator

Figure 3. Integration of the orthogonal frequency division multiplexing (OFDM) simulator with QualNet

the radio tries to receive the signal. SINR is calculatedfrom the strength of the signal and the interference plusnoise in the channel. The baseline QualNet model doesnot currently model Doppler, multipath, or frequency off-set effects. Hence, the integration of the OFDM model iscarried out as follows: the QualNet nodes original SINR,SINRin , is fed into the detailed OFDM model. In combina-tion with the user-specified multipath, Doppler, frequencyoffset, and relative speed of the nodes calculated in Qual-Net, a dynamic channel is generated. The OFDM modelis then simulated, and the resulting SINRSINRout, seen

at the receiver basebandis used to calculate the loss de-fined in (2).This loss value, as we will explain later, is thenstored in a table inside QualNet. The new SINR result isthen used to calculate whether the packet includes errors.

SINRout =Signalin Loss

Noisein +Loss. (2)

As mentioned earlier in section 2.2, simulation of theOFDM model is time-consuming. While bit-level simu-lation in wireless environments is desirable, large-scalenetwork simulations must trade off between simulationexecution time and accuracy. Simulation time of this in-tegrated system is considerably reduced via two methods:simulation of only a portion of thedata frameanda caching

mechanism to cache similar scenarios.While evaluating the OFDM simulator, it is noticed

that the simulated resulting receiver SINR value does notchange significantly (within 1%) after the simulation ofa certain number of OFDM symbols. This is because thetransmission duration is less than the coherence time. Thecoherence time of the channel is a measure of the speedat which the channel characteristics change. This durationof this time is on the order of multiple frame transmis-

sions. Using this fact, the OFDM simulation was stoppedafter the SINR measurement stabilized, which was after40 OFDM symbols. This reduced simulation time, as typ-ical packet transmission length might last for hundreds ofOFDM symbols. For example, a 1472-byte packet modu-lated at 6 Mbps would transmit 503 OFDM symbols.

More significantly, a caching mechanism was devel-oped to take advantage of scenarios with similar SINRand channel conditions. That is, after running the OFDMsimulator at a given SINR and channel condition, the lossresulted from that runwould be saved. The loss value is the

signal strength loss; it becomes part of the noise. When asimilar SINR and channel condition transmission occurs,the resulting SINR is calculated using (2), with the lossvalue previously cached. The loss is cached initially in-stead of the resulting SINR value because the granularityof theinputSINR is rounded to thenearest integer;an inputSINR of 11.5 dB and 12.4 dB would map to the same lossvalue, not the same SINR. Caching the original resultingSINR valuewould be inaccurate because of the large gran-ularity, but using the loss calculated, a realistic, effectiveSINR value that includes the effects of device and channelis obtained. With the new SINR value, the correspondingBER is retrieved. The error probability for the packet isthen calculated, and the packet is tested for error. Simu-

lation runtime is sped up considerably with this cachingmechanism.

5. Simulation Studies

5.1 MANET Simulation Scenarios

This section quantifies the effects of OFDM radio andchannel modeling on typical scenarios used in the perfor-mance evaluation of MANETs. Scenarios for this compar-




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Table 1. Parameters used in QualNet and orthogonalfrequency division multiplexing (OFDM) simulation

Channel frequency (GHz) 5.2Effective subcarriers 48Data rate (Mbps, auto-rate fallback) 24Antenna gain (dBi) 0BPSK (dBm)

TX power 20.0RX sensitivity/threshold 85.0

QPSK (dBm)TX power 19.0RX sensitivity/threshold 83.0

16-QAM (dBm)TX power 18.0RX sensitivity/threshold 78.0

64-QAM (dBm)TX power 16.0RX sensitivity/threshold 69.0

ison are created as follows: each scenario is configured

with a stationary 50-node network uniformly distributedover a 1000 1000-m and 500 500-m terrain. Twenty-five nodes are randomly chosen to be constant bit rate(CBR) sources, each of which generates 512-byte datapackets to a randomly chosen destination at a rate of 5,10, 20, 40, and 60 packets per second for 180 sec. Thenetwork uses ad hoc on- demand distance vector routing(AODV) [15] for each CBR source to discover a routeto the destination. Each data point represents the aver-age value from seven runs with different random num-ber seeds. With different seeds, the node placement andCBR sessions in the network differ. Other common pa-rameters are listed in Table 1. The transmission powerand receiver sensitivity are taken from SMC2755W

EZ Connect 802.11a Wireless Access Point UserGuide (http://www.smc.com/drivers_downloads/library/SMC2755W_MN.pdf), an actual commercial implemen-tation of the IEEE 802.11a.

In this evaluation, two data rate types, 24 Mbps andauto-rate fallback (ARF), were chosen. First, every nodeis set to transmit only at 24 Mbps. This corresponds tothe 16- QAM modulation in the OFDM model. Second,each node uses the ARF [16] algorithm for automatic datarate adjustment to best match the varying channel condi-tions. The basic idea of the ARF protocol is to keep trackof the number of successful transmissions, and only aftera number of successful attempts, the sender sends the datapackets at the next higher data rate. The sender also keeps

a timer; when the timer expires, the sender tries to send thenext packet at the next higher data rate. The protocol de-creases thesenders transmissionrate eitherwhen it missestwo consecutive acknowledgments (ACKs) or when it failsto receive an ACK immediately after raising the transmis-sion data rate. The timer value, 60 msec, is experimentallyfound to be optimal in Holland, Vaidya, and Bahl [17].For ARF to achieve good performance with the RTS/CTSframe exchange, the sending node shouldcount themissed

Table 2. Set of parameters used in orthogonal frequencydivision multiplexing (OFDM) simulation

Fading model RayleighDoppler spread (Hz) 250.0Number of cyclic prefix 20Number of cyclic postfix 1Path loss exponent 3

CTS packet as an ACK failure when a node fails to re-ceive theCTS after an RTS transmission. Thus, twomissedCTSpackets would lead toa subsequent data rate decrease.Using the ARF rate-adjusting algorithm, the OFDM con-stellation will vary between BPSK, 4-QAM (QPSK), 16-QAM, and 64-QAM, depending on the data rate.

Table 2 contains a list of parameters fed into the OFDMmodel by QualNet, considered as typical outdoor con-ditions. All the variables are chosen to mimic the IEEE

802.11a parameters.

5.2 Packet Delivery Ratio and MAC TotalRetransmission with Fixed Data Rate

Figures 4 and 5, respectively, show the packet deliveryratio (PDR) and the number of retransmissions with thefixed data rate observed in simulation with and withoutthe integrated OFDM model in the 1000 1000-m ter-rain environment. As shown in Figure 4, the PDR perfor-mance of the integrated OFDM model simulation is signif-icantly lower than that of the original abstract model whenthe transmitting data rate is fixed. As the network loadincreases, the PDR decreases considerably due to packet

transmission errors and channel congestion. At the high-est packet rate scenario, the integrated OFDM simulationproduces PDR that is only two-fifths of that of the origi-nal abstract model. In Figure 5, the difference between theintegrated model and abstract model is obvious. The num-ber of MAC packets dropped per second per node attemptsis significantly higher for the integrated OFDM simula-tion. This correlates well with the lower PDRs depicted inFigure 4. At 40 and 60 packets per second per flow, thenumber of MAC retransmission attempts is closer to thatof the abstract model. This substantial difference in sim-ulation results with the integrated OFDM model clearlydemonstrates the need for the detailed simulation of phys-ical layer models in network system-level simulation.

Figures 6 and 7 show the same simulation setup butwith a 500 500-m terrain size. The general trend is sim-ilar to that of the larger grid scenario. Packet delivery ratiois higher than that of the larger terrain size as the densersmaller scenario induces less multiple-hop CBR sessionsandhighersignalquality. This equates to the lower numberof MAC packets dropped per second at the same sessionsend rate. It is clear from Figures 6 and 7 that as the sessionrate increases, the disparity between the abstract QualNet




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24Mbps, Packet Delivery Ratio (1000m x 1000m)

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0.60.7

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5 10 20 40 60

pkts. per second per flow

PDR

QualNet with OFDM

Model

Basic QualNet

Figure 4. Packet delivery ratio (PDR) with and without the detailed orthogonal frequency division multiplexing (OFDM) model using24-Mbps data rate transmissions (1000 1000 m)

24 Mbps, MAC Packets Dropped per Second per Node

(1000m x 1000m)

05

101520253035404550

5 10 20 40 60


NumberofMAC

pkts.

D

roppedperSecondper

Node

QualNet with OFDMModel

Basic QualNet

Figure 5. Number of MAC packets dropped per second per node with and without the detailed orthogonal frequency divisionmultiplexing (OFDM) model using 24-Mbps date rate transmissions (1000 1000 m)

model and the detailed OFDM model increases. A com-parison of Figures 5 and 7 shows that network saturationhas not been reached yet, even at the highest send rates in

the smaller terrain size simulation.

5.3 Packet Delivery Ratio and MAC TotalRetransmission Using Auto-Rate Fallback

Figures 8 and 9 show the same metrics discussed in theprevious section with ARF. The results are quite differentwhen each node uses ARF as its data rate control algo-rithm. For the two different simulation models, the PDR

and the number of MAC packets dropped per second pernode matches each other closely, as shown in Figures 8and 9. Because ARF adjusts data rates based on channel

conditions, in sparse network scenarios, ARF can lowerthe nodes transmitting data rate to ensure packet deliverywithout overloading the transmission medium. By com-paring the PDR of Figure 4 with that of Figure 8 at 5, 10,and 20 packets per second per flow, it is easily seen thatARF takes advantage of the sparse traffic to ensure packetdelivery. It is also clear that thegradual PDRdecrease fromthe OFDM model in Figure 4 is caused by other wirelessnetwork traffic interference.ARF adapts to light-load noisy




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24Mbps, Packet Delivery Ratio (500m x 500m)

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PDR

QualNet with OFDM

Model

Basic QualNet

Figure 6. Packet delivery ratio (PDR) with and without the detailed orthogonal frequency division multiplexing (OFDM) model using24-Mbps data rate transmissions (500 500 m)

24 Mbps, MAC Packets Dropped per Second per Node

(500m x 500m)

0

5

10

15

20

25

30

5 10 20 40 60


NumberofMAC

pkts.

Dropped

perSecondperNode


Basic QualNet

Figure 7. Number of MAC packets dropped per second per node with and without the detailed orthogonal frequency divisionmultiplexing (OFDM) model using 24-Mbps data rate transmissions (500 500 m)

environments well. However, as the packet rate increases,ARF is actually detrimental to PDR performance. Noticethat the PDR performance in Figure 4 at 40 and 60 packets

per second per flow is higher than that of Figure 8. By low-ering thedatarate,ARF, in highly congested environments,causes longer packet transmission duration and, in effect,longer delays and more queue overflows. This leads to alower PDR ratio in congested scenarios when comparedwith the fixed data rate setting.

Figures 10 and 11 show the same ARF simulation setupbut with a 500 500-m terrain size. The trends are againsimilar to that of the larger terrain size. Both the basic

abstract QualNet model and the detailed OFDM modelmatch each other closely. The drop in PDR is more gradualas the CBR session rate increases when compared to the

larger terrain size environment, and the network is not yetsaturated, even at 60 packets per second per flow.

While the observation of ARF performance itself isinteresting, the difference in simulation results with andwithout the detailed OFDM model is much smaller thanthat shown in the previous section. Although this particu-larcase does notseem to require thedetailed OFDM modelto predict the network performance, there is no good wayto determine whether the detailed physical layer model is




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Auto Rate Fallback, Packet Delivery Ratio (1000m x1000)

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PDR

QualNet with OFDM

Model

Basic QualNet

Figure 8. Packet delivery ratio (PDR) with and without the detailed orthogonal frequency division multiplexing (OFDM) model usingauto-rate fallback (1000 1000 m)

Auto Rate Fallback, MAC Packets Dropped per Second per Node

(1000m x 1000m)

0

510

15

2025

303540

4550

5 10 20 40 60


Numbe

rofMAC

pkts.

Dropped

pe

rSecondperNode


Basic QualNet

Figure 9. Number of Mac packets dropped per second per node with and without the detailed orthogonal frequency divisionmultiplexing (OFDM) model using auto-rate fallback (1000 1000 m)

essential, as it highly depends on the protocol characteris-tics. Furthermore, the quantification of such difference in

predicted performance cannot be done unless simulationresults with and without the detailed model are compared.

5.4 Runtime Performance of Caching Technique

As previously noted, link-level OFDM simulation is verycomputationally expensive. While detailed simulation ofevery bit of the network is desirable, one cannot expect touse the OFDM simulator to simulate every packet in the

network for large MANETscenarios. Our integration tech-nique (described insection 3) of caching thesignal loss and

partial transmission simulation alleviates the problem as itcaptures the interactions of the wireless channel with theradio device and yet still maintains a reasonable executiontime toallow forlargeMANETsimulations.Figures12and13 depict theexecution speedup benefit of using thecache-detailed model method as opposed to using the OFDMmodel to simulate every single bit in the network. In thesimulation, a stationary 25-node network is placed over a500 500-m terrain. Fifteen nodes are randomly chosen




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Auto Rate Fallback, Packet Delivery Ratio (500m x 500m)

0

0.1

0.2

0.3

0.4

0.5

0.60.7

0.8

0.9

1

5 10 20 40 60


PDR

QualNet with OFDM

Model

Basic QualNet

Figure 10. Packet delivery ratio (PDR) with and without the detailed orthogonal frequency division multiplexing (OFDM) modelusing auto-rate fallback (500 500 m)

Auto Rate Fallback, MAC Packets Dropped per Second per Node

(500m x 500m)

0

5

10

15

20

25

30

5 10 20 40 60


NumberofMAC

pkts.

Dropped

perSecondperNode


Basic QualNet

Figure 11. Number of Mac packets dropped per second per node with and without the detailed orthogonal frequency divisionmultiplexing (OFDM) model using auto-rate fallback (500 500 m)

to be CBR sources, each of which generates 512-byte datapackets to a randomly chosen destination at a rate of 1, 5,10, 20, 40, and 60 packets per second and using AODV

routing. The simulation was stopped when the executiontime per simulation second stabilized. One can easily seethe benefits of the caching; the improvement in executiontime ranges from 2000 to more than 75,000 times.

Thecaching-detailed OFDM model is able to scale withtheabstract model.The x-axis in Figure14 shows theaver-agenumber of signals lockedon by each receiver. Detailedsimulation of every bit is infeasible for MANETs, whilethe cache-detailed simulation model is able to scale even

with the abstract model. When the OFDM model was usedto simulate every bit of the network, it took more than 370hours for eachnode to just lockon to and simulate just over

1000 radio signals.

5.5 Validation of Caching Technique

The caching-detailed OFDM simulation is only valid if thecache saved does not compromise the accuracy of the sim-ulation. Our next experiment validates this model with thesimulation of all bits using the link-level OFDM modelin the network. Since simulating every bit in the network




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Every Bit Detailed Simulation Model

0.0E+00

2.0E+05

4.0E+05

6.0E+05

8.0E+05

1.0E+06

1.2E+06

1 5 10 20 40 60


ExecutionTimeperSimulatio

Second(second

s)

Figure 12. Execution time per simulation second, without caching results from the detailed orthogonal frequency divisionmultiplexing (OFDM) model

Cache Detailed Simulation Model

12

12.1

12.2

12.3

12.4

12.5

12.6

12.7

12.8

1 5 10 20 40 60


Exec

utionTimeperSimulation

Second(seconds)

Figure 13. Execution time per simulation second, with caching results from the detailed orthogonal frequency division multiplexing(OFDM) model

is very computationally expensive, as shown in the previ-ous section, the experiment scenarios were limited to light

network traffic situations. The same 25-node network isplaced on the 500 500-m terrain. Three nodes are ran-domly chosen to be CBR sources, each of which generates512-byte data packets to a randomly chosen destination ata rate of 0.1, 0.5, 0.8, and 1 packet per second using AODVrouting for 30 seconds. Even with these lighter traffic sce-narios, simulating all bits with the OFDM model with justthree CBR sessions and one packet per second per flowtook more than 32 hours on a modern Intel 2.4-GHz ma-

chine. Figure 15 shows the number of physical layer sig-nals received successfullyandforwardedto theMAC layer

per second. When a physical layer frame is deemed errorfree by the OFDM simulator or the cache OFDM model,it passes that frame to the MAC layer for higher layer pro-cessing.Theexperimentshows that using thecacheOFDMsimulation model results in no more than 2% difference inthe number of signals received successfully by the radiowhen compared to using the OFDM simulator to simulateevery bit of the network. Hence, our cache model is welljustified, and we are able to speed up execution time to




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Execution Time Comparison

0

50

100

150

200

250

300

350

400

0 10000 20000 30000 40000 50000

Number Of Signals Locked On By Receivers

ExecutionTime(Minutes

Default QualNet

Cache OFDM Model

Simulate All Bits OFDM Model

Figure 14. Comparison of execution times

Cache OFDM Model Validation

0

50

100

150

200

250

300

350

0.1 0.5 0.8 1


NumberofSignalsReceived

Succes

sfullyandForwardedtoMA

LayerperSecond

Cache OFDM Model

Simulate All Bits OFDMModel

Figure 15. Validation of caching technique against simulation without caching

scale similarly to execution with the abstract model whilestill preserving thefidelity andaccuracyof detailed OFDMradio and wireless channel simulation.

6. Conclusion

This article has presented the effects of detailed OFDMand channel modeling on the performance evaluation ofhigher layer protocols. Our integration of an OFDM simu-lator with QualNet provided a realistic yet efficient modelof the propagation and device layer for network perfor-mance analysis without compromising simulation accu-

racy. The results show that device and wireless channelcan affect packet delivery ratios and even point out a defi-ciency of the ARF protocol. Traditionally, radio engineers

have analyzed theperformanceof theirdesignsagainst oth-ers only on point-to-point performance evaluations undervariouschannel conditions. The integration brings an accu-rate physical layer model to dynamic network simulationthat includes the effects of path loss, shadowing, multi-path, Doppler fading, and delay spread to allow protocoland radio designers to evaluate the effects of their designson a full-scale system level with an eye for cross-layer in-teractions. In addition, the integration delineates a method




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in which simulators of dramatically different time granu-larities are combined using simple APIs.

In terms of performance, our integration techniqueand cleanly defined interface are clearly beneficial in anyMANET networking studies.A link-level OFDM model tosimulateeverybit in thenetworkis toocomputationallyex-

pensive and leads to an unacceptably long execution time.Our technique scales along with the basic abstract modeland still captures the essence of the radio device and itsperformance characteristics in varying wireless channels.The results show that significant benefits can be obtainedfrom our caching technique, while careful evaluation ofwhat to cache must be properly understood.

With advances in antenna,modulation, andcoding tech-nology, it becomes increasingly important for higher levelnetwork layers to understand their interactions with thephysical device and media. It is equally important for de-signers to understand the innovations that are being madein each layer of the network stack and to understand howthese innovations might complement or conflict with their

designs. Future work on the integration method includesenhancementsto thecachingscheme,dynamicchannelandfading characteristics using detailed 3-D terrain models,and movement of the nodes.

7. Acknowledgment

We would like to thank Alireza Mehrnia and BabakDaneshrad for the initial versions of the OFDM simula-tor. This work was supported in part by the Office of NavalResearch through the MINUTEMAN project under con-tract number N00014-01-C-0016.

8. References

[1] Misra, Vishal, Weibo Gong, and Don Towsley. 2000. Fluid-basedanalysis of a network of AQM routers supporting TCP flows withan application to RED. In Proceedings of ACM/SIGCOMM.

[2] Yung,Tak-Kin, Jay Martin, Mineo Takai, and Rajive Bagrodia. 2001.Mixed fluid flow and packet level simulation models for largescale networks. In Proceedings of SPIE, August.

[3] Takai,Mineo,Jay Martin, andRajive Bagrodia. 2001. Effectsof wire-less physical layer modeling in mobile ad hoc network. In Pro-ceedings of ACM MobiHoc, October, pp. 87-94.

[4] Buck,J. T., S.Ha, E.A. Lee, andD. G.Messerschmitt. 1994.Ptolemy:A framework for simulating and prototyping heterogeneous sys-tems. International Journal of Computer Simulation 4:155-82.

[5] Riley, G. F., M. H. Ammar, R. M. Fujimoto, K. Perumalla, andDonghuaXu.2001. Distributednetworksimulationsusing thedy-

namic simulationbackplane. In Proceedings of International Con-ference on Distributed Computing Systems (ICDCS01), April.

[6] Xu, Donghua, G. F. Riley, M. H.Ammar, and R. M. Fujimoto. 2001.Split protocolstack networksimulations using the dynamic simu-lation backplane. In Proceedings of the Ninth International Sym-

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[7] Zhou,Junlan,ZhengrongJi,Mineo Takai,and Rajive Bagrodia. 2003.Maya: A multi-paradigm network modeling framework. In Pro-ceedings of the 17th Workshop on Parallel and Distributed Simu-lation (PADS 03).

[8] IEEE.1999. IEEE802.11WG, part11: WirelessLAN medium accesscontrol (MAC) andphysical layer(PHY) specifications,standard,IEEE,August.

[9] IEEE.1999. IEEE802.11WG, part11: WirelessLAN medium accesscontrol (MAC) and physical layer (PHY) specifications: High-speed physical layer in the 5 GHz band, supplement to IEEE802.11 standard, September.

[10] Van Nee, Richard, and Ramjee Prasad. 2000. OFDM for wirelessmultimedia communications. Boston:Artech House.

[11] Terry, John, and Juha Heiskala. 2001. OFDM wireless LANs: A the-oretical and practical guide. Indianapolis, IN: Sams Publishing.

[12] Bharghavan, Vaduvur, Alan Demers, Scott Shenker, and LixiaZhang. 1994. MACAW: A media access protocol for wireless

LANs. In Procedings of ACM SIGCOMM.[13] Yeung, Gavin, Mineo Takai, Rajive Bagrodia, Alireza Mehrnia, and

Babak Daneshrad. 2004. Detailed OFDM modeling in networksimulation of mobile ad hoc networks. In Proceedings of the 18thWorkshop on Parallel and Distributed Simulation (PADS04),May.

[14] Bajaj, Lokesh, Mineo Takai, Rajat Ahuja, and Rajive Bagrodia.1999. Simulation of large-scale heterogeneous communicationsystems. In Proceedings of MILCOM99, November.

[15] Perkins, Charles E., and Elizabeth M. Royer. 1999. Ad-hoc on-demand distance vector routing. In Proceedings of 2nd IEEEWorkshop on Mobile Computing Systems and Application, pp.90-100.

[16] Kamerman,A.,and L. Monteban.1997.WaveLAN-II:A highperfor-mancewirelessLAN for the unlicensedband.Bell Labs Technical

Journal 2 (3): 113-18.[17] Holland, Gavin, Nitin Vaidya, and Paramvir Bahl. 2001. A rate-

adaptive MAC protocol for multi-hop wireless networks. In Pro-ceedings of ACM MobiCom 2001, July.

Gavin Yeung is a PhD student in the Computer Science Depart-

ment at the University of California, Los Angeles.

Mineo Takai is a principal development engineer in the Com-

puter Science Department at the University of California, Los

Angeles.

Rajive Bagrodia is a professor in the Computer Science Depart-

ment at the University of California, Los Angeles.

http://sim.sagepub.com/

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