Presentation on theme: "CHANNEL ESTIMATION AND LOW COMPLEXITY DETECTION FOR HIGH SPEED WLANs"— Presentation transcript:
1 CHANNEL ESTIMATION AND LOW COMPLEXITY DETECTION FOR HIGH SPEED WLANs A THESISSubmitted byMUTHURAJA NIn partial fulfillment for the award of the degreeofMASTER OF SCIENCE (BY RESEARCH)FACULTY OF INFORMATIONAND COMMUNICATION ENGINEERINGANNA UNIVERSITY : CHENNAIAPRIL 2006
2 Outline Introduction Channel estimation for MIMO-OFDM systems MIMO Detection methodsSummary
3 Effective data throughput (in Mbps) IEEE evolutionWi-Fi Performance SummaryThe performance of the various PHY extensions of can be analyzed according various criterion.RF Band: , b, and g are strictly operationally confined to the 2.4 GHz band while a is operational in the 5 GHz band. The n standard is relevant to both bands since there is a clear agreement that the 2.4 GHz band cannot meet the capacity requirements presumed for this market. Hence, products that can intelligently switch between the bands will likely be the norm in the near future. Regulation in some countries are still needed to harmonize the 5 GHz band operation.Data Rate: The family of standards supports peak data rates ranging from 1 to 600 Mbps in discrete steps. There has been a constant upgrade of the peak data rates based on popularity and demand. Moreover, new methods and techniques developed primarily by academics are being incorporated into the family to meet the perceived demands.Transmission Technique: While spread spectrum techniques are used in b, a/g/n use OFDM. Also, n combines Multiple Input Multiple Output (MIMO) and OFDM to achieve the 600 Mbps targets. The MIMO technique is an elaborate trick with multiple antennas that has some stunning effects on data rates.Range: The effective distance over which an off-the-shelf network will work is about 100 meters, which is about as far as healthy people can hear when yelling out to each other. Range is managed with techniques like rate adaptation, which is used in all the extensions to keep the connections alive and working as the distance between the transmitter and receiver increases. Higher power products for niche applications can achieve higher ranges, and these have been used for outdoor and niche applications. Directive antennas can also increase the radio range, but these might be more relevant to point-to-point or backhaul applications.802.11802.11b802.11a802.11g802.11nFrequency2.4 GHz5.X GHz2.4/5.x GHzData rate (in Mbps)1,2Min-1Mbps6,9,12,18,24,36,48,541,2,5.5,11,6,9,12,18,24,36,48,54Up to 600 MbpsModulationFHSS, DSSSDSSSOFDMDSSS / OFDMMIMO-OFDMMaximum Range300 ftEffective data throughput (in Mbps)0.5,1532>100Channel BW22 MHz20 MHz20/40 MHz3-26
4 Evolution of IEEEThe effective application rate offered by the existing WLANs was still far lesser than the LAN (by 2004)WLAN standard has evolved from the basic IEEE (which supports to 1Mbps) to 54 Mbps by modifying PHY and MAC layer.IEEE a/g was the standard widely used for WLANs (by 2004)IEEE n taskgroup (TGn) was formed with the goal of increasing the application throughput to atleast 100Mbps by making changes in the PHY and MAC layerSupport to Legacy stations
5 IEEE 802.11n – Main Features 802.11n WLAN Efficient OFDM Multiple antennasMACTwo elements in the advanced n PHY dramatically increase data rates.One of these elements is a collection of mechanisms that increases the efficiency of the OFDM signal. One of these tightens interference control as it reduces the number of subcarriers for guard bands. Guard bands are reserved parts of the channel’s spectrum, at the extreme upper and lower edges, which are left unoccupied as a countermeasure against interference with services in neighboring channels in the spectrum. Another is some coding details that have been called on to further enhance the efficiency of the OFDM signal. These are mandatory changes that ensure an upper limit of 65 Mbps for data rates. The 40 MHz-wide channel option will probably be used in the 5 GHz band to further increase data rates.The second element of change is the use of multiple antennas. The most popular use of multiple antennas is Multiple Input Multiple Output (MIMO), which is also called Spatial Multiplexing. The use of multiple antennas can lead to rather stunning increases in the channel data rates. Currently, two antennas are mandatory at the AP side and one antenna is sufficient for the client or user side. Most traditional WLAN devices, like laptops, will have multiple antennas but some low-cost clients like handsets might have only one antenna.AggregationBlock AckAdvanced Power saveMIMO2 Antennas at AP and1 antenna at the userReduced guard intervalReduced guard bandModulation and Coding
6 IEEE n standardThe basic technology for increasing the rate is the use of multiple antennas for spatial multiplexing (SM). However, transmit diversity with space time coding, beamforming, and SVD based schemes are also proposed as optional features.The standard proposes the use of upto 4 antennasThe number of useful subcarriers is increased to 52There is an optional mode with 40MHz BW, wherever the regulatory body allows itShortened GI, code rate upto 7/8, advanced FEC coding are other optional features.Rates supported vary from 6.5Mbps to 500 Mbps.
8 MIMO-OFDM systemsThe orthogonal frequency division multiplexing (OFDM) transmission scheme is an efficient technique to combat ISI and simplify the equalization problemThe use of multiple antennas at transmitter and/or at the receiver helps in many ways such as diversity gain, spatial multiplexing and beamformingThe MIMO signaling can easily be overlayed on an OFDM based system.The MIMO signaling treats each subcarrier in OFDM as an independent narrowband frequency flat channel. It can be viewed as N parallel MIMO systems operating with flat fading channel coefficients.MIMO-OFDM system offers an increase in rate by employing SM at the same time as we combat the ISI problem in an elegant way
9 important parameter in Spatial MultiplexingGeneral MIMO exampleData stream 1(OFDM symbols)...Uncorrelated channelsAPTx1Tx2Rx1Rx2h11h12h21h22Data stream 2(OFDM symbols)...Received signal at the antennas is the combination of the spatially multiplexed data from the different transmit antennas.Matrix channel is animportant parameter inanalysis and design
10 MIMO-OFDM systems Challenges in MIMO-OFDM systems: Channel estimation (CE)SynchronizationMIMO detectionChannel estimation – to estimate the channel coefficients corresponding to all transmit receive antenna pair and on all subcarrier positions.MIMO detection also becomes computationally intensive as it has be applied on all the subcarriers.
11 Contribution of the thesis The main focus is towards analysing the various PHY layers proposed for nThe thesis covers two portionChannel estimation for nMIMO detection schemesThe performance of several preambles used for MIMO channel estimation and different schemes are analysedLow complexity way of implementing the CE schemes are also discussed by exploiting the SFCF.System performance of various MIMO detection schemes is presented.
12 Section2: Channel estimation for 802.11n systems
13 Outline Channel estimation in MIMO-OFDM systems Different kind of preamblesTM methodSM method and its variationTO methodSO methodPreambles in IEEE nTGn sync: SM (twice)WWise: SOEWC: TO
14 Outline IEEE 802.11n channel model Different MIMO preambles Preambles in IEEE n proposalChannel estimation schemesLSLMMSEInterpolation based estimation (LCCE)TMMSEML methodComplexity of CE schemesPerformance for various CE schemesMean square errorSystem performance in terms of BERMSE results for TGn channels for TGn sync, WWise and EWC proposals
15 TGn channel model IEEE 802.11n TGn channel model MIMO channel model for indoor and typical office environment in LOS and NLOS conditionsCluster based channel model
16 Cluster based channel model Modification of Saleh Valenzula model - By adding of arrival statisticsThe complete impulse response with respect to both time and angleThe time of arrival of the ith cluster is Tiτj,i is the time of arrival of the ijth path.The clusters and the rays within the cluster decay in amplitude and timeThe decay rate of cluster and the rays are Λ, λ,
17 Cluster based channel model The angle of arrival statisticsAngular domain Impulse responseΘi ~ uniform [0,2π] - mean angle of arrival in ith cluster.ωij - correspond to the jth ray angle in ith cluster,modeled as Laplacian distributed random variable.Each cluster has the following angular statisticsMean angle of arrival (AoA)Mean angle of departure (AoD)Azhimuth angluar spread (AS)Elevation angular spread
18 Power Angular spectrum The angle of arrival statistics within a cluster - Laplacian distributionFigure 2.2: Laplacian PASσ - Angular spread
19 Cluster based channel model The complex correlation coefficients – PAS, AS, AoA and Individual tap powersRXX – crosscorrelation function between the real/imag partsRXYcross correlation between the real part and imaginary partTo calculate the numerical values of correlationmatrices we use a Matlab program developed and distributed by L. Schumacher
20 TGn channel model - Channel parameters ModelsMax. delay spread(in ns)RMS delay spread NLOS (ns)K factor (in dB)No.of.ClustersLOSNLOSA-∞1B80152C20030D390503E73010064F1050150For each cluster in a channel model,AStxASrxAoAAoDare specified
21 Power delay profileCluster 2Cluster 1150ns rms delay spread
26 ESTIMATE THE CHANNEL COEFFICIENTS AT ALL SUBCARRIER POSITIONS MIMO OFDM systemReceived signals at kth subcarrier in a simple 2x2 systemRx. Ant 1:Rx. Ant 2:In matrix representationESTIMATE THE CHANNEL COEFFICIENTS AT ALL SUBCARRIER POSITIONS
27 Time Multiplexed method S0S1……..SN-2SN-1Ant 1S0S1……..SN-2SN-1Ant 2MLTF1MLTF2Time Multiplexed (TM) methodIn each MLTF – Transmission from one antennaSimple channel estimation – LS estimate
28 Time Multiplexed method Training symbol at ‘k’ the subcarrier from the two antennasAnt 1Ant 2MLTF1MLTF2The received signal at kth subcarrier
29 Time Multiplexed method The channel estimates at Kth subcarrier is given byMean square error :MSE in dBSNR in dBAverage transmit powerTotal Energy requiredMSE is inversely proportional to SNR
30 Subcarrier Multiplexed method S2S1S3……….SN-2………..SN-1Ant1Ant 2MLTF1Subcarrier multiplexed methodOdd subcarriers – Transmitted from Antenna 1Even subcarriers – Transmitted from Antenna 2Interpolation needs to be done to estimate channel on all subcarriers
31 Subcarrier Multiplexed method Training symbol at the kth subcarrier from the two antennasAnt 1Ant 1Ant 2Ant 2MLTF1MLTF1The received signal at kth subcarrier
32 Subcarrier Multiplexed method In all odd subcarrier positionsIn all even subcarrier positionsEven subcarriers of channel coefficients correspondingto TX.ant 1 are obtained by Interpolation.Odd subcarriers of channel coefficients correspondingto TX.ant 2 are obtained by Interpolation.
33 Subcarrier Multiplexed method Average transmit powerTotal Energy required
34 Subcarrier Multiplexed method - twice Training symbol at ‘k’ the subcarrier from the two antennasAnt 1Ant 1Ant 2Ant 2MLTF1MLTF2MLTF1MLTF2The received signal at kth subcarrier
35 Time orthogonal method S0S1………….SN-2SN-1S0S1………….SN-2SN-1Ant 1S0S1…………SN-2SN-1-S0-S1………..-SN-2-SN-1Ant 2MLTF1MLTF2
36 Time Orthogonal method Training symbol at ‘k’ the subcarrier from the two antennasAnt 1Ant 2MLTF1MLTF2The received signal at kth subcarrier
37 Time Orthogonal method The channel estimates at Kth subcarrier is given byMean square error :MSE in dBSNR in dBAverage transmit powerTotal Energy requiredMSE is inversely proportional to SNR
43 Packet structure Ant 1 Ant 2 LSTF LLTF LSIG HT-SIG HT STF HTLTF1 DATA8μs8μs4μs8μs2.4μs7.2μs7.2μsAnt 2LSTFLLTFLSIGHT-SIGHTSTFHTLTF1HTLTF2DATA8μs8μs4μs8μs2.4μs7.2μs7.2μsMIMO Channel estimationIs done during this partCDDSimplified PPDU format in 2x2 system-TGn sync proposal for IEEE n
44 Long preamble structure in TGn sync Time domain viewGISet 1Set 1GISet 2Set 2Ant 1GISet 2Set 2SISet 1Set 1Ant 2HTLTF1 (7.2μs)HTLTF2 (7.2μs)Subcarrier domain viewK=0Set 1S-26S-24…..S-2S2…..S24S26Set 2S-25S-23…..S-1S1…..S23S25
45 Least squares channel estimation Linear relationship between the channel and the received signalSolving the linear equations leads to Least squares (LS) channel estimatesMean square error (MSE) is directly proportional to the noise variance
47 WWise preamble – Mixed mode Ant 1SS20LS20SIG-MMLS20SIG-NDATAAnt 2SS20LS20SIG-MMLS20SIG-NDATA8μs8μs4μs8μs4μsCyclic delay of 400nsCyclic delay of 1600nsCyclic delay of 3100ns
48 WWise preamble – Green field mode Ant 1SS20LS20SIG-NDATAAnt 2SS20LS20SIG-NDATA8μs8μs4μsCyclic delay of 400nsCyclic delay of 1600ns
49 Training symbol at ‘k’ the subcarrier from the two antennas WWISE methodTraining symbol at ‘k’ the subcarrier from the two antennasAnt 1Ant 1Ant 2Ant 2MLTF1MLTF1The received signal at kth subcarrier during first repetition
50 CE method for WWISEThe channel estimates are obtained by
51 MSE closed formThe spaced frequency correlation is obtained from F.T of PDP
53 EWC preamble – Mixed mode Ant 1L-STFL-LTFLSIGHTSIGHTSTFHTLTF1HTLTF2DATAAnt 2L-STFL-LTFLSIGHTSIGHTSTFHTLTF1HTLTF2DATA8μs8μs4μs8μs4μs4μs4μsCyclic delay of 200nsCyclic delay of 400ns
54 EWC preamble – Green field mode Ant 1L-STFHTLTF 1HTSIGHTLTF2DATAAnt 2L-STFHTLTF 1HTSIGHTLTF2DATA8μs8μs8μs4μsCyclic delay of 200nsCyclic delay of 400ns
55 EWC Time domain view GI Set 1 Set 1 GI -Set 1 Ant 1 GI Set 2 Set 2 GI HTLTF1 (7.2μs)HTLTF2 (4μs)Set 2 is Cyclic shiftedby 400ns of Set 1Subcarrier domain viewSet 1-Set 1S1-26S1-25S1-24……...S124S125S126-S1-26-S1-25-S1-24……...-S124-S125-S126Set 2Set 2S2-26S2-25S2-24……..S224S225S226S2-26S2-25S2-24……..S224S225S226HTLTF 1HTLTF 2
56 Mixed mode – Least squares Ant 1Ant 2HTLTF1HTLTF2Received signal at Kth subcarrier is given byThe channel estimates at Kth subcarrier is given by
57 Green field mode – Least squares Received signal at Kth subcarrier is given byThe channel estimates at Kth subcarrier is given by
58 Enhanced CE schemes LMMSE Interpolation based estimation (LCCE) TMMSE ML method
59 LMMSE channel estimation The spaced frequency correlation in the channelis used to get better estimate compared to LS estimate.Bx1 vectorAutocorrelation, ‘R’ matrix captures thefrequency domain correlation in the channelLMMSE estimate :
60 LMMSE channel estimation LMMSE filter requires the autocorrelation matrix and the noise varianceImperfect estimation of R and the noise variance leads to the irreducible error floor in the MSEComputational complexity of the LMMSE scheme is very high as it requires B 2 multiplications and a matrix inversionA block wise LMMSE - Reduce the complexity at the expense of performance degradation
61 LMMSE channel estimation The spaced frequency correlation in the channelis used to get better estimate compared to LS estimate.Bx1 vectorAutocorrelation, ‘R’ matrix captures thefrequency domain correlation in the channelLMMSE estimate :
62 Blockwise LMMSE BL – Reduced block length B – Original Block length NB – Number of BL blokcs in BThe autocorrelation matrix block of length BLLMMSE estimatefor p th block
63 Interpolation based Low complexity channel estimation (LCCE) Interpolation based channel estimationCorrelation among the adjacent subcarriers are used without the need for the autocorrelation matrix, R and huge computations.Channel estimates are got by weighted average of LS estimates and the interpolation estimates
64 Block diagram HTLTF 1 HTLTF 2 RX. 1 RX. 2 LS est RX. 1 LS est RX. 2 IntInt1-WW1-WW
65 Low complexity channel estimation The final channel estimates is the weighted averageof direct LS estimate and the interpolated estimateSimple linear interpolation filter – Low computational overheadLinear interpolation :Weights of the linear interpolation are chosen to be powers of 2to use shifting instead of multiplicationOther Interpolations like cubic, spline can be done – Complexity increases
66 Low complexity channel estimation Weight valueLow SNR regionHigh SNR regionW = 0.53.5 dB improvement over LS estimationError floor depends upon RMS delay spreadW>0.5<3.5 dB improvement over LS estimationError floor is less compared to W=0.5W<0.5>3.5dB improvement over LS estimation.(But not more that 4.25dB)Error floor is high compared to W=0.5Error floor is directly proportional to the RMS delay spread
67 MSE closed form for linear interpolation method The spaced frequency correlation is obtained from F.T of PDP
68 Truncated MMSE (TMMSE) - CE Smoothing LS estimates by weight values obtainedfrom MMSE solutionThe MMSE solution matrix Vp of a truncated R matrix is obtained as followsRp is the correlation matrixof dimension PxPThe middle row of Vp matrix is used as weight vector
69 TMMSE - CEFilter the LS CEs using these weight values as filter coefficientsThere is a loss in performance compared to LMMSE,due to truncation and smoothing with less number of weightsTo reduce complexity the modulus of the complex weights is consideredand quantized to the nearest power of 2LCCE method is a special case of TMMSE method when the weights are real
70 ML methodML channel estimation – with assumption that the maximum lengthof the channel impulse response is not greater than the guard time.Step 1:Step 2:Step 3:Step 4:Where, F is the Fourier matrix andFred is the reduced Fourier matrix whose dimension is L x LSuitable only for symbol spaced channel
71 Computational complexity MethodComlex multiplicationsComplex additionsOtherLSB-LMMSE-52B2 + B(B-1)BFinding common LMMSE filterO(B3) complex multiplicationsLMMSE-13NBBL2 + BNB(BL-1)BLO(BL3) complex multiplicationsLCCE(Linear intp)2B2B shifting operation.TMMSE-PB(P+1)B(P-1)Finding the P filter coefficientsB is the number of subcarrier; For TGnSycn B=52, WWISE & EWC B = 56
72 Results for all the methods Performance of various preamblesPerformance - TGn sync preamblePerformance - EWC preamblePerformance - WWISE preambleEffect of various channel estimation schemes on system performance interms of BER & PER – Section3
73 per receive antenna per subcarrier Simulation model,The SNR used here refers to signal to noise ratioper receive antenna per subcarrierThe performance measure is the MSE of the channel estimateare the ideal and estimated CEs on the kth subcarrier
74 Performance of various preambles Noise Power in dBMSE performance of different preambles in channel D, NLOS conditionsMSE
94 BER performance – Uncoded system BER performance of MMSE detection with different CE schemes for 2x2 system in channel D, NLOS.
95 BER performance – TGn sync system QPSK – ½ rate BER performance of LC MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS.
96 PER performance – TGn sync system QPSK – ½ rate PER performance of LC MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS.
97 Complexity of CE schemes MethodComlex multiplicationsComplex additionsOtherLSB-LMMSE-52B2 + B(B-1)BFinding common LMMSE filterO(B3) complex multiplicationsLMMSE-13NBBL2 + BNB(BL-1)BLO(BL3) complex multiplicationsLCCE(Linear intp)2B2B shifting operation.TMMSE-PB(P+1)B(P-1)Finding PxP LMMSE filterO(P3) complex multiplications
98 Section2 : MIMO Detection methods for 802.11n >> A simple uncoded system>> TGn Sync system
99 MIMO Detection schemes Decorrelator / ZFMMSESuccessive Interference Cancellation (SIC)ZF/MMSE VBLAST (Ordered SIC)Maximum Likelihood (ML)Explain in detail about each of these schemes.
100 MIMO Detection schemes Maximum Likelihood (ML)Optimum and most complex detection methodZero-Forcing(ZF)Pseudo inverse of the channel, simplest detection methodMinimum mean-squared error (MMSE) : Intermediate complexity and performance
101 MIMO Detection schemes V-BLASTOrdered successive interference cancellation (SIC) detectorFinding Nulling Solution(Zero Forcing )(MMSE filter )OrderingChoosing the best channelis the jth row of GNullingNullifying the effect of the channel faced by kth stream
102 Non-feedback MIMO Receivers (contd..) SlicingQuantizing the nullified symbolwith appropriate constellationCancelingCancel the effect of detectedstream from the received signalFinding newchannel matrixFind new channel matrix by replacing the columnscorresponding to the detected streams with zerosIterationRepeat from step 1 with new channel matrixuntil all the streams are detected
103 V-BLAST – 2 x 2 Example Let the received signal be where Finding Nulling SolutionUsing zero forcingor MMSE solutionOrderingFind the energy of all the rows of GChoosing the best channelNullingAssume k=2, then the nulling vector
104 V-BLAST – 2 x 2 ExampleNullifying the effect of the channel faced by 2th streamSlicingQuantizing the nullified symbolwith appropriate constellationCancelingCancel the effect of detectedstream from the received signalFinding newchannel matrixIterationRepeat from step 1 another time to get the 1st stream.
105 Mean square error in detection The mean square error (MSE) between the transmitted data symbols and the output of the detection algorithm is a good measure for the performance of MIMO detection algorithmsMSE easy to derive for MIMO detection.From simulation, the reduction in MSE leads to BER reduction.
106 Low complexity MIMO detection We need to employ N independent MIMO detectors in a MIMO system with N subcarrier.The frequency correlation among the subcarriers can be used to reduce the complexity of the MIMO-OFDM systemInstead of independently employing MIMO detector in all subcarriers, only the solution for the MIMO detector on alternate subcarrier positions are foundThe solution for the other subcarriers is found by interpolating the solutions obtained for the neighboring subcarriers.
107 Low complexity MIMO detection Linear interpolation using weights which are simple to implement can be used.Let k-1 and k+1 be the subcarrier positions where the direct solutionlet k be the subcarrier position in which the solution is obtained by linear interpolationWhere Vk is the matrix solution for MIMO detection50% reduction in the complexity when compared to the normal MIMO-OFDM detection methodsThis idea can be used for ZF, MMSE, MMSE-SIC, ZF-SIC detection method.It cannot be directly applied to the VBLAST based detection schemes, since the order in which the detection is performed varies for each subcarrier.
108 Complexity comparison Number of complex multiplication is considered
109 Complexity comparison Comparison of computational complexity for various MIMO detection schemes
111 Simulation results and discussion Uncoded systemSimulation results for MIMO detection algorithmsEffect of CE on the system performanceSimulation parametersNumber of Subcarriers, N =64Cyclic prefix = 16 samplesBW = 20MHzQPSK modulationTGn channel D – NLOSResults presented in terms of MSE performance, BER and PER.
112 MIMO detection schemes for 2x2 and 4x4 Figure:MSE performance of various MIMO detection schemes for 2x2 system in channel D, NLOSFigure: MSE performance of various MIMO detection schemes for 4x4 uncoded system in channel D, NLOS
113 Low complexity MIMO detection scheme MSEBERFigure : MSE performance of various low complexity MIMO detection schemes for 2x2 uncoded system in channel D, NLOS NLOS.Figure : BER performance of various MIMO detection schemes for 2x2 uncoded system in channel D, NLOS
114 BER performance with different CE Figure : BER performance of MMSE detection with different CE schemes for 2x2 system in channel D, NLOS
116 TGn Sync - Simulation results InfobitsTGn syncTxTGn syncRx.ChannelChannelEstimation using preamblesFigure : Simulation modelParameterValueCRModulationQPSKNt x Nr2x2Payload1000 bytesResults presented in terms of BER and PER
117 MIMO detection schemes – BER & PER Figure : BER performance of various MIMO detection schemes for 2x2 TGn sync system in channel D, NLOSFigure : PER performance of various MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS
118 LC-MIMO detection schemes – BER & PER Figure : BER performance of LC-MIMO detection schemes for 2x2 TGn sync system in channel D, NLOSFigure : PER performance of LC-MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS
119 Effect of CE schemes on system performance Figure: BER performance for various CE schemes with MMSE detection for 2x2 TGn sync system in channel D, NLOSFigure: PER performance for various CE schemes with MMSE detection for 2x2 TGn sync system in channel D, NLOS
120 Effect of CE schemes on system performance for all channel models Figure : Gp Loss in performance of 10-5 BER for different CE method on all channel modelsGp is defined as the Loss in performance in terms of SNRat a cut off point of 10-5 BER
122 SummaryVarious MIMO detectors are discussed and their performance in uncoded system in terms of MSE and BER is presented based on simulation.The effect of CE on the performance of uncoded MIMO system is also presented.A low complexity solution for MIMO-OFDM detection is proposed and it reduces the computational complexity by 50%.The performance of the TGn sync system is presented for various MIMO detection methods in terms of BER and PER.It is shown by simulations that the LC MIMO detectors result in very less performance degradation for practical channel conditions. Thus, the LC MIMO detectors can be used for IEEE n proposals as they reduce the computational complexity load at the receiver.The effect of various CE schemes on the performance of IEEE n TGn sync proposal is presented in terms of BER and PER.The results indicate that the LS scheme results in about 3 dB loss in performance at 10-5 BER point, while the low complexity CE schemes such as LCCE, TMMSE have less performance degradation. Thus, the TMMSE and LCCE CE schemes can be used for IEEE n proposals leading to fewer computations and less performance degradation.
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