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Simulation ofWiMAX Systems
Onsy Abdel AlimProf., Faculty of Engineering,
Elect. Eng. Dept.,Beirut Arab University,
Hiba S. AbdallahEng., Faculty of Engineering,
Elect. Eng. Dept.,Beirut Arab University,
Lebanonhiba [email protected]
Azza M. ElaskaryEng., National Center forRadiation and Technology,
(NCRRT)Egypt
ABSTRACT-WiMAX (Worldwide Interoperability forMicrowave Access) has the potential to impact all forms oftelecommunications. In a fixed wireless communication, WiMAXcan replace the telephone company's copper wire networks, thecable TV's coaxial cable infrastructure while offering InternetService Provider services. In its mobile variant, WiMAX has thepotential to replace cellular networks. However, it still facingreal challenge for low complexity and efficient systemimplementation. It supports NLOS environment with high datarate transmission and high mobility up to 125km/hr.
In this paper, two main issues are discussed. The first onepresents models for simulating OFDM WiMAX system inSimulink including channel estimation and equalizationsubsystems in MATLAB functions. Next, the effect of channelestimation error on the performance of MIMO VBLASTreceivers in uncorrelated Rayleigh flat fading channels isinvestigated.
keywordsWiMAX, MIMO, VBLAST, OFDM, ChannelEstimation, Equalization, Doppler Effect
I. BACKGROUND
WiMAX is based on Wireless Metropolitan Area Networking(WMAN) standards developed by the IEEE 802.16 group andadopted by both IEEE and ETSI HIPERMAN group. It provides veryhigh data throughput over long distance in a point-tomultipoint andline of sight (LOS) or non-line of sight (NLOS) environments.WiMAX can provide seamless wireless services up to 20 or 30 milesaway from the base station. The IEEE 802.16 group subsequentlyproduced 802.16a, which include NLOS applications in the 2GHz-11GHz band, using an orthogonal frequency division multiplexing(OFDM)-based physical layer. The WiMAX forum has two differentsystem profiles: a new standard in 2004 based on IEEE 802.16-2004,OFDM PHY, and is called the fixed system profile [1], the other onebased on IEEE 802.16e-2005 scalable OFDMA PHY, called themobility system profile [1]. The Mobile WiMAX specificationdefines multiple-input multiple-output (MIMO) option, which is akey feature in mobile WiMAX.OFDMA allows multiple-antenna operations to be performed onvector-flat sub-carriers. Complex equalizers are not required tocompensate for frequency selective fading. OFDMA therefore, isvery well suited to support smart antenna technologies. In fact,IMOOFDM/OFDMA is envisioned as the foundation for next-generation broadband communication systems. This paper isorganized as follows. The end-to-end WiMAX system model
including channel estimation and equalization to facilitate evaluationof performance in single-input single-output fixed/mobile system isintroduced in section II. An overview of MIMO and MIMO WiMAXis presented in section III, while in section IV the performance ofdifferent MIMO receivers under the effect of imperfect channelestimation is simulated. In section V, we state some conclusions andpossible future work.
II. FIXED/MOBILE WIMAX USING SINGLEANTENNA
The WiMAX physical layer is based on OFDM. OFDM is thetransmission scheme of choice to enable high-speed data, video, andmultimedia communications. The WiMAX forum has two differentsystem profiles: one based on IEEE 802.16-2004, OFDM PHY,called the fixed system profile; the other one based on IEEE 802.16e-2005 scalable OFDMA PHY, called the mobility system profile. Inthis section, we briefly describe these standards, the channelestimation and equalization approaches that are used in this paper,the WiMAX system simulation and finally results and discussion areprovided.
A. WIMAXPHYSICAL LAYER
Fixed WiMAXOFDM-PHYFor this version the FFT size is fixed at 256, which 192
subcarriers used for carrying data, 8 used as pilot subcarriers forchannel estimation and synchronization purposes, and the rest usedasguard band subcarriers. Since the FFT size is fixed, the subcarrierspacing varies with channel bandwidth. When larger bandwidths areused, the subcarrier spacing increases, and the symbol timedecreases. Decreasing symbol time implies that a larger fractionneeds to be allocated as guard time to overcome delay spread.WiMAX allows a wide range of guard times that allow systemdesigners to make appropriate trade-offs between spectral efficiencyand delay spread robustness. For maximum delay spread robustness,a 25 percent guard can be used, which can accommodate delayspreads up to 16 pts when operating in a 3.5MHz channel and up to 8pts when operating in a 7MHz channel as shown in table 1 [1].
978-1-4244-1754-4/08/$25.00 ©2008 IEEE
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Table 1. Table Represent WiMAX Phy. Layer Specifications
Mobile WiMAX0FDMA-PHYMobile WiMAX is intended for the 2.3 GHz, 2.5 GHz and
3.5GHz spectra. The system is defined so that the user can travel atspeeds between 0-125 km/h. The theoretical upper limit for the bitrate in WiMAX, given a bandwidth of 1OMHz, is 31 Mbps indownlink and 23 Mbps in uplink [1]. The base stations have a typicalcoverage up to an 8 km radius in a NLOS environment. In MobileWiMAX, the FFT size is scalable from 128 to 2,048. Here, when theavailable bandwidth increases, the FFT size is also increased suchthat the subcarrier spacing is always 10.94 kHz. The subcarrierspacing of 10.94 kHz was chosen as a good balance betweensatisfying the delay spread and Doppler spread requirements foroperating in mixed fixed and mobile environments. This subcarrierspacing can support delay-spread values up to 200 pts and Vehicularmobility up to 125 kmph when operating in 3.5 GHz. Also, asubcarrier spacing of 10.94 kHz implies that 128, 512, 1024, and2,048 FFT are used when the channel bandwidth is 1.25MHz, 5MHz,10MHz, and 20MHz, respectively. The OFDM symbol frequencydomain description in WiMAX is illustrated in Fig. 1 [1].
Figure I OFDMA Sub-Carrier Structure
In Fig. 1, total of 256 subcarriers are spread to four parts.There are 192 subcarriers for data transmission, 8 subcarriers forpilot tone, 52 subcarriers for guard bands, and 1 subcarrier for DCin every OFDM symbol.
B. CHANNEL ESTIMATION & EQUALIZATIONThere are two maj or kinds of channel estimators that are
found in literature, namely pilot assisted and blind estimation. Intraining sequence methods or non-blind methods, the transmitteddata andtraining sequences known to the receiver are embedded into theframe and sent through the channel. Main advantages of trainingsequences are the conceptual simplicity of the method, which isfacilitated in some new communication standards that includes
frames for sending training sequences. Obviously, the drawbackis because the time-slots occupied in the transmission of thesetraining sequences reduces throughput.
In blind methods, mathematical or statistical properties oftransmitted data are used. They are bandwidth efficient however,they are significantly slow to converge and require importantcomputational capacity. A mixture of these two, where a blindmethod with limited training symbols is used, is called semi-blindtechnique. The semi-blind methods use information from bothtraining sequence and statistical properties of the transmittedsignal, which makes them more robust than the blind methodswhile they still require less training compared to the non-blindmethods. Traditional one-dimensional channel estimationtechniques for the OFDM systems can be summarized as follows:Least Squares (LS), Minimum Mean squared Error (MMSE) andLinear MMSE (LMMSE). LS estimators are very simple toconstitute, but they suffer from MSE in low SNR conditions.MMSE, based on time domain estimators, are high complexityestimators that provide good performance in sampled-spacedchannels, but limited performance in non-sample spaced channelsand high SNR conditions. The third one, LMMSE provides goodperformance in both sampled and non-sampled channels [1].
C. WiMAXSYSTEMSIMULATIONMATLAB and Simulink are used for modeling theWiMAX
OFDM physical layer [1,4]. The WiMAX End-to-End Model isshown in figure 2. In the transmitter, the input output data vectoris read in and written to MATLAB workspace after each majorfunction block then it is compared to the standard test vector andit was identical.Additional functions such as Inverse Fast Fourier Transformed(IFFT) and cyclic prefix addition to the OFDM Symbol have beenadded and modeled to ensure compatibility. The frequencychange due to the Doppler Effect depends on the relative motionbetween the source and receiver. WiMAX specifications areconsidered to implement the channel. In our simulation, the "lowvalue" Doppler frequency is z70 Hz corresponds to a mobilespeed of 20 Km/hr at 3.5GHz carrier frequency and a "highvalue" Doppler shift corresponding to the operation at 3.5 GHzfor a velocity of 120 Km/hr is z400 Hz.
In the simulation, the channel has been modeled as a MultipathRayleigh fading channel with additive white Gaussian noise(AWGN), at Simulink multipath fading box, the calculatedDoppler shift has been inserted. The receiver implementation isan inversion of all the transmitter functions with addition ofchannel estimation and equalization parts. The channel estimationbased on comb type pilot arrangement is studied through differentalgorithms for both estimating channel at pilot frequencies andinterpolating the channel. The estimation of channel is based onLS and LMS while the channel interpolation is done using linearinterpolation, second order interpolation, low-pass interpolation,and spline cubic interpolation [1]. The effect of differentinterpolation approaches based on LS and LMS were tested withlow and high relative velocities.
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Figure 2 WiMAX End-to-End Simulink Model
Figure5. BER vs. SNR in dB for LMS with small Dopplershift (low relative velocity)
Figure3. BER vs. SNR in dB for LS and LMS various interpolation criteria infixed conditions
Figure 4. BER vs. SNR in dB for LS with small Doppler shift(low relative velocity)
Figure6. BER vs. SNR in dB for LS with high Doppler shift(high relative velocity)
Figure7. BER vs. SNR in dB for LMS with high Doppler shift(high relative velocity)
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D. RESULTSAND DISCUSSIONWe have simulated the proposed scheme for WiMAX
communication system specifications. The AWGN and MultipathDoppler shift models are used as testing environment. Comparingthe performances of all schemes by measuring BER versus SNRwith setup as shown in Fig.5, the results are shown in figures 3 to7. Figure 3 shows that Channel estimation based on LS algorithm,with the linear interpolation, the second order interpolation, thespline cubic interpolation and the low-pass interpolation,respectively perform about the same as LMS algorithm in fixedconditions. From figures 4 to 7, it is clear that LS estimatorperformance degrades at high relative velocity and LMS hasbetter performance in mobile case. In general, the channelestimator that performed the best in terms of lowest BER is theLowpass interpolation algorithm.
III. MULTIPLE ANTENNAS IN WiMAXSYSTEMS
A. MIMODemand for capacity in wireless communications has been
rapidly increasing worldwide. Nevertheless, the availablespectrum is limited and capacity needs cannot be met without asignificant increase in spectral efficiency. Advances in channelcoding make it feasible to approach the Shannon capacity limit insystems with single antenna links (SISO-Single-Input Single-Output).
As a solution to the high capacity requirements of futurewireless systems, a lot of awareness has been drawn to multiple-input multiple-output (MIMO) wireless systems with multipleantennas at both the transmit and receive sides by increasing thenumber of antennas at both the transmitter and the receiver [2]. Ingeneral, capacity grows linearly with the number of transmitantennas, Nt, as long as the number of receive antennas, Nr,satisfies the condition Nr > Nt as shown in Fig.8. As a result,MIMO systems are excellent candidates for highdata-rate futuremobile systems, such as 3G, WiMAX and beyond.
1- 4 4- * w~1 4- 4 -
t3~~~~~~~-
D ,~~~~ J I
S __,------- T|-0-r1
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Figure 8. Outage capacity for various antenna configuration
B. MIMO WIMAXOne of the WiMAX system profiles is the simple Space-
Time coding (STC) scheme proposed by Alamouti for transmitdiversity on the downlink. In the IEEE 802. 16e-2005specifications, this scheme is referred to as "Matrix A" whichimproves the reliability of data transmission for mobile modems.A MIMO cell site sends multiple, redundant copies of a datastream to the receiver to increase the probability that some oftheom mav suerviveblthe pyicalpathbetnmwenframnmisiodnandm
conditions are most likely to occur while the terminal is highlymobile, with rapid signal fading and multipath reception.
Figure 9.Comparison of Alamouti/MRC with 2x2 spatialmultiplexing [3]
The second multiple antenna profile included in WiMAXsystems is the 2x2 MIMO technique based on the so called"Matrix B". This system performs spatial multiplexing (SM) anddoes not offer any diversity gain from the transmit side. But itdoes offer a diversity gain of 2 on the receiver side when detectedusing Maximum-Likelihood (ML) detection [2]. SM system sendseach data frame only once allowing full independent usage ofantennas, however it gives limited benefit and is not always thebest transmission scheme for a given BER target. Withoutnaturally occurring multi-path, MIMO Matrix B signal candestructively combine, resulting in no signal at the receiver.Natural multi-path is common in highly cluttered environments,such as dense urban areas.
The tradeoff between Matrix A and Matrix B will be left tofuture investigations. Figure 10 shows the results on anuncorrelated Rayleigh fading channel when the Alamouti/MRCscheme uses 16-QAM and the spatial multiplexing scheme usesQPSK (4 bits per symbol period in both cases). It can be observedthat the zero-forcing (ZF) receiver does not exploit the diversityof the spatial multiplexing scheme and that the slope of its BERcurve is only half that of the Maximum Likelihood (ML) receiveras shown in Fig.9. The other major observation is that the slope ofthe Alamouti/MRC scheme is twice as large as that of the spatialmultiplexing ML receiver, which is due to the diversity factor of4 for the former and of 2 for the latter [3].
IV. MIMO WITH IMPERFECT CHANNEL ESTIMATION
An example of a pure multiplexing scheme is the Vertical-Bell Labs layered space-time (V-BLAST). The main idea of theV-BLAST architecture is to split the information bit stream intoseveral substreams and transmit them in parallel using a set oftransmit antennas (the number of transmit antennas equals thenumber of substreams) at the same time and frequency. At the Rxend, each Rx antenna sees all the transmitted signals, which aremixed due to the nature of the wireless propagation channel.Using appropriate signal processing at the receiver, these signalscan be separated so that the matrix wireless channel is
reception in a good enough state to allow reliable decoding. These
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transformed into a set of virtual parallel independent channels(provided that the multipath is rich enough). Previous researchesanalyzed tight closed-form BER approximation of MIMO ZFreceiver in an uncorrelated Rayleigh flat fading channels underthe assumption that perfect CSI is available at the receiver.However, noisy channel estimates arise in any practical system,
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............. . .. , ... .. ..
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;;~~ ~ ~~~~~~~~~X5 2;B---S------;--X;;X-;U-UL;0--
LH..N..pS..'_t. - t _ .. _.
A. SIMULATIONRESULTSThe performance ofV-BLAST ZF receiver in the presence of
different degrees of CSI imperfection through simulation ispresented.
Figure 1O. BER performance comparison of QPSK MIMO V-BLAST ZF receiver with Nt = 2, Nr = 3 versus Nt = 2,Nr4 and Nt = 2. Nr = 6 for 0%. 1%. 5% nmse error
so it is important to evaluate the performance of MIMO receiversin the presence of imperfect CSI.The standard baseband system model is given by:
r=Hs+n, Ki) |where s denotes the transmitted data symbol vector consisting ofNt symbols each with a constellation size M and E[SSH]=ES Nt I,while n - N(O, No INt) is the additive Gaussian noise vector, andES is the transmitted symbol energy per transmit antenna. In orderto retrieve the transmitted data symbol vector at the receiver,accurate knowledge of the complex channel gain matrix H isrequired. However, in practice, imperfect channel estimates arisein any practical system. The noisy channel state information ismodeled as [2]:
H = 1 --e H+eQ. (2Where eQ is the estimation error that is uncorrelated with H,the entries of Q are independent and identically distributed(i.i.d.) zeromean complex Gaussian random variables withunity variance. The factor p = 1 - e2 represents the correlationcoefficient between the actual channel gain and its estimate,which is assumed to be the same for all channel gains.Parameter e E [0,1] is the measure of quality of channel stateinformation. A smaller "e" indicates higher correlation andhence better CSI quality. The normalized mean square error(nmse) of the channel estimation is defined as
where ij ij h^ h , represent the (i,j)th element of H and H^respectively. It can be easily shown that the normalized meansquare is related to e by:
mnse- (tw-1eIz )I
The effect of the number of transmit antennas Nt on the BERperformance of VBLAST-ZF QPSK in the presence of imperfectCSI is investigated with D=Nt-Nr. Fig.10 and fig.1 1 show that asNt increases the BER gets worse with the same value of D andnmse, while the BER performance only depends on D and SNRwhen perfect CSI is assumed. Intuitively the reason for thedependence on Nt is that the inter-stream interference cannot becancelled perfectly in the presence of channel estimation error. AsNt increases, the number of data streams increases. Given thesame transmit power per antenna, the source of the interferenceincreases. As a result the BER performance gets worse as Ntincreases.Also, when there is no channel estimation error, as Eb/No -* cc,
BER -, o, while an error floor is expected at high SNR due tothe channel estimation error. Moreover as Nt and/or e increases,the SNR value corresponding to the starting point of the errorfloor decreases. The previous results are compared with theresults obtained using the pilot channel estimation as shown inFigure 12 [2]. It shows that, for low SNR (-5dB), theperformance of V-BLAST ZF using pilot estimation is equivalentto 5% nmse error, when the SNR increases, the BER performanceapproaches that using perfect CSI.
V. CONCLUSIONS AND FUTURE WORK
In the first part, WiMAX top level simulink with all systemdetails have been implemented for simulation purpose. Channelestimation with different interpolation approaches forfixed/mobile OFDM system with parameters from WiMAXstandards have been illustrated. The results showed that theDoppler shift had a greater impact on the performance betweenthe different channel estimators and interpolation approaches. Inthe second part, the performance of MIMO VBLAST ZFreceivers over uncorrelated Rayleigh flat fading channels in thepresence of channel estimation error is investigated. It is foundthat contrary to the case when there is no channel estimation
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.D RS t<. NrA S E.- 1 9,.,
.2. N --E5--------------
NM '-3 E--
N, 2 P"
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error, the BER performance is not only a function of D = Nr-Nt, but also a function of Nt.Besides, for the same value of D and error "e" the BERperformance gets worse as Nt increases. Another impact ofimperfect channel estimation is that when SNR is high, BER doesnot approach zero but approaches an error floor whose valuedepends on Nt, Nrand e.
[3] www.mathwork.com Recorded Webinar "From a Wireless StandardsDocument to an Executable Model using MATLAB and Simulink" July14, 2005.
[4] B.Muquet, E.Biglieri, H. Sari: "Mimo Techniques for Mobile WiMAXSystems", SEQUANS Commu. White paper, 2007.
[5] 0. Abdel Alim, A. El Naggary:"Performance of MIMO AntennaTechniques on IEEE802.16E", ICICT2007, Dec. 16-18, Cairo, Egypt.
For Future work, RF planning for different geographicalenvironments is to be considered for WiMAX systemsimplementation [5]. An adaptive technique for switching betweenSpatial Multiplexing and Diversity techniques in MIMO OFDMas defined in the standard WiMAX systems will be investigated.Also, the effect of imperfect channel estimation will beconsidered
Figure 11. BER performance of 16QAM MIMO V-BLAST ZFreceiver with D=2 andD=4.
Figure 12 BER performance ofV-BLAST ZF receiver for QPSKmodulated signal for 2x2 system.
REFERENCES
[1] 0. Abdel Alim, N. Elboghdadly, M.Ashour, A. Elaskary:" ChannelEstimation and Equalization for Fixed/Mobile OFDM WiMAX Systemin Simulink", Mobilware'08, Feb. 12-15, 2008, Austria.
[2] H. S. Abdallah, I. A. Ghaleb, S. Abou. Chahine: "The Effect ofImperfect Channel Estimation on the Performance of Layered SpaceTime Receivers", IEEE ISSPIT'07, Dec. 15-18, 2007, Cairo, Egypt.
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