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PDSCH吞吐量基于Non-Codebook MU-MIMO Transmission Mode 9 (TM9)

This example demonstrates how to measure the physical downlink shared channel (PDSCH) throughput performance in a multiuser multiple-input multiple-output (MU-MIMO) scenario with LTE Toolbox™. It models non-codebook based transmission mode, TM9, with block diagonalization [1]. This example supports both frequency division duplexing (FDD) and time division duplexing (TDD) schemes. It also supports the use of Parallel Computing Toolbox™ to reduce the effective simulation time.

Introduction

In a MU-MIMO scenario, due to the simultaneous transmission of data to multiple users, inter-user interference will be present at the receiver. Inter-user interference at the receiver can be canceled using precoding techniques at the transmitter. Two linear precoding techniques for MU-MIMO transmission are channel inversion and block diagonalization. This example uses block diagonalization precoding. This example measures the PDSCH throughput in a MU-MIMO scenario for a number of signal-to-noise ratio (SNR) points. For information on how to model single-user MIMO (SU-MIMO) in LTE look at the following example:PDSCH Throughput for Non-Codebook Based Precoding Schemes: Port 5 (TM7), Port 7 or 8 or Port 7-8 (TM8), Port 7-14 (TM9 and TM10)

A simple MU-MIMO block diagram with the default simulation configuration parameters used in the example is shown in the following figure.

Simulation Configuration

The simulation parameters for the base station and users are configured in this section. The example is executed for a simulation length of two frames for a number of SNR points. IncreaseNFramesto increase the simulation time and produce statistically significant throughput results. Use the variableSNRInto set the SNR, it can be an array of values or a scalar. As per the constraints in LTE [2], this example supports a maximum of 4 users with a maximum of 4 layers across all users. The number of maximum layers per user is 2. The number of transmit antennas should be greater than or equal to the total number of receive antennas across all users.

NFrames = 2;% Number of framesSNRIn = [8 14];% SNR range in dBNUsers = 2;% Number of active usersNTxAnts = 2;%的数量在eNodeB天线% Specify UE-specific parametersmuNumLayers = [1 1 1 1];% Number of layers for a maximum of 4 usersmuNumRxAnts = [1 1 1 1];% Number of receive antennas for a maximum of 4 usersmuCodeRate = [0.5 0.5 0.5 0.5];% Code rate for a maximum of 4 usersmuModulation = {'16QAM';'16QAM';'16QAM';'16QAM'};% Modulation for a maximum of 4 users

The set of parameters required for TM9 is specified below. This example does not perform DCI format decoding; theDCIFormatfield is included for completeness. The cell arraymuPDSCHstores the PDSCH transmission configuration structure for all users.

% Initialize cell arrays of PDSCH transmission configuration structures,% transport block sizes and coded transport block sizes.muPDSCH = cell(NUsers,1); trBlkSizes = cell(NUsers,1); codedTrBlkSizes = cell(NUsers,1); simulationParameters = [];% clear simulation parameterssimulationParameters.NDLRB = 50; simulationParameters.PDSCH.PRBSet = (0)'; simulationParameters.PDSCH.DCIFormat ='Format2C'; simulationParameters.PDSCH.TxScheme ='Port7-14'; simulationParameters.PDSCH.NTxAnts = NTxAnts; simulationParameters.DuplexMode ='FDD';% 'FDD', 'TDD'simulationParameters.TotSubframes = 1;% PDSCH configuration structure for users based on the common and% user-specific parametersncw = zeros(NUsers,1);foruserIdx = 1:NUsers simulationParameters.PDSCH.TargetCodeRate = muCodeRate(userIdx); simulationParameters.PDSCH.Modulation = muModulation{userIdx}; simulationParameters.PDSCH.NLayers = muNumLayers(userIdx);% Initialize W to zerosimulationParameters.PDSCH.W = zeros(muNumLayers(userIdx),NTxAnts);% Downlink reference measurement channel configurationenb = lteRMCDL(simulationParameters);% PDSCH transmission configuration structure for usersmuPDSCH{userIdx}= enb.PDSCH;% Number of codewords for usersncw(userIdx) = length(muPDSCH{userIdx}.Modulation);% Store transport block sizes for userstrBlkSizes{userIdx} = muPDSCH{userIdx}.TrBlkSizes;end% Assign redundancy version sequencervSequence = muPDSCH{1}.RVSeq;

Print a summary of some of the more relevant simulation parameters.

hMultiUserParameterSummary(enb,muPDSCH,muNumRxAnts);
Parameter summary for TM9 MU-MIMO Transmission ------------------------------------------------------------------ Duplexing mode: FDD Transmission mode: TM9(MU-MIMO) Transmission scheme: Port7-14 Number of downlink resource blocks: 50 Number of allocated resource blocks: 1 Number of transmit antennas: 2 ------------------------------------------------------------------ Number of Transmission layers for UE-1: 1 Number of codewords for UE-1: 1 Number of receive antennas for UE-1: 1 Modulation codeword 1: 16QAM Transport block sizes codeword 1: 208 208 208 208 208 208 208 208 208 208 Code rate codeword 1: 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 ------------------------------------------------------------------ Number of Transmission layers for UE-2: 1 Number of codewords for UE-2: 1 Number of receive antennas for UE-2: 1 Modulation codeword 1: 16QAM Transport block sizes codeword 1: 208 208 208 208 208 208 208 208 208 208 Code rate codeword 1: 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088 0.5088

Propagation Channel

The channel model configuration parameters for the channel between the eNodeB and the users are stored in the cell arraymuChannel. The set of common parameters for each channel is initially specified. The parameters defined here are used withlteFadingChannelduring subframe processing.

muChannel = cell(NUsers,1); channel = struct; channel.DelayProfile ='EPA';% Delay profilechannel.MIMOCorrelation ='Low';% Multi-antenna correlationchannel.NTerms = 16;% Oscillators used in fading modelchannel.ModelType ='GMEDS';%瑞利衰落模型类型channel.InitPhase ='Random';% Random initial phaseschannel.NormalizePathGains ='On';% Normalize delay profile powerchannel.NormalizeTxAnts ='On';% Normalize for transmit antennas

The channel sampling rate depends on the FFT size used in the OFDM modulator. This can be obtained using the functionlteOFDMInfo.

ofdmInfo = lteOFDMInfo(enb); channel.SamplingRate = ofdmInfo.SamplingRate;% Independent channel configuration parameters for each userchanSeeds = [1111 2222 3333 4444];% Channel seed for a maximum of 4 usersdopplerFreq = [5 50 25 15];% Doppler frequency for a maximum of 4 usersforuserIdx = 1:NUsers muChannel{userIdx}= channel; muChannel{userIdx}.Seed = chanSeeds(userIdx);% Channel seedmuChannel{userIdx}.NRxAnts = muNumRxAnts(userIdx);% Number of receive antennasmuChannel{userIdx}.DopplerFreq = dopplerFreq(userIdx);% Doppler frequencyend

Processing Chain

To determine the throughput at each SNR point, the subframe-by-subframe PDSCH processing chain includes:

  • Calculating the Precoding Matrix -A perfect channel estimate is used to calculate the precoding matrix for each user. A detailed explanation of this step is provided in the next section.

  • Updating Current HARQ Process -Separate HARQ processes are used for each user.

  • Creating Transmit Waveform -Separate PDSCH symbols are generated for each user. The PDSCH symbols are precoded with the calculated precoding matrix. The precoded PDSCH symbols corresponding to the UEs are combined and OFDM modulated.

  • Channel Modeling -Pass the waveform through a fading channel to each user and add noise (AWGN)

  • Performing Synchronization and OFDM Demodulation -Separately carried out for each user. Offset the received symbols to account for a combination of implementation delay and channel delay spread. OFDM demodulate the symbols.

  • Decoding the PDSCH -Separately carried out for each user. Perfect channel estimate is assumed at the receiver for decoding operations. Obtain an estimate of the received codewords usingltePDSCHDecodeto demodulate and descramble the recovered PDSCH symbols for all transmit and receive antenna pairs.

  • Decoding the DL-SCH and Storing the Block CRC -Separately carried out for each user. Pass the vector of decoded soft bits tolteDLSCHDecode, which decodes the codeword and returns the block CRC error used to determine the throughput of the system.

Precoding Matrix Calculation

The precoding matrix for each user needs to be computed based on the channel estimates between the eNodeB and users. The precoding matrix is calculated using the block diagonalization method. For the computation of the precoding matrix, channel state information (CSI) is required at the transmitter. In this example, for the sake of simplicity, perfect knowledge of the channel between the eNodeB and the users is assumed at the base station.

For TDD, the channel estimates between the eNodeB and the users are estimated in the last UL subframe before a DL subframe. These channel estimates are used to calculate the precoding matrix,W. All subsequent DL subframes (including special subframes) until the next UL subframe are precoded with matrixW.

For FDD, there is a delay of one subframe between the calculation ofWand the subframe where it is used. For example,Wused in DL subframenhas been calculated with the channel estimates obtained in DL subframen-1.

The functionhMultiUserPrecodingMatrixcalculatesWusing the following steps:

  • Obtain a perfect channel estimate for the considered subframe for all users

  • Average the channel estimates for all the allocated RBs

  • Compute the precoding matrix using the block diagonalization method as per theblkdiagbfweightsfunction.

Note that for an allocation of a single resource block, the precoding matrix will usually be well matched to the channel conditions, with little deviation from the optimal precoding. But as the allocation size increases, the precoding matrix takes into account the average of the channel conditions over the whole allocation. This averaging causes a deviation from the optimal precoding matrix. Therefore, you can expect a degradation in performance as the size of the resource allocation increases.

Processing Loop

The 'for' loop for SNR points processing is included below. To enable the use of parallel computing for increased speed use 'parfor' instead of 'for' in the loop. This requires the Parallel Computing Toolbox. If this is not installed 'parfor' will default to the normal 'for' statement.

%初始化变量用于模拟和analysismaxThroughput = zeros(length(SNRIn),NUsers); simThroughput = zeros(length(SNRIn),NUsers); harqProcesses = cell(NUsers,1);% Initialize cell array for constellation plotrxConstellation = cell(numel(SNRIn),NUsers,2);% Copy the channel cell array and cell array of PDSCH transmission% configuration structure to optimize parallel processing (only if running% the example with Parallel Computing Toolbox)muChannelInit = muChannel; muPDSCHInit = muPDSCH;% During the simulation, some fields of enb will be updated, make a copy to% reinitialize it when simulating each SNR pointenbInit = enb;% For TDD precalculate vector of subframe types: D, S and U for downlink,% special, and uplink, respectivelyifstrcmpi(enb.DuplexMode,'TDD') subframeType = char(10,1); initialSubframeNo = enb.NSubframe;forsNo=0:9% for all subframes in a frameenb.NSubframe = sNo; duplexInfo = lteDuplexingInfo(enb); subframeType(sNo+1) = duplexInfo.SubframeType(1);% first char: D, S or Uendenb.NSubframe = initialSubframeNo;end% CFI can be a scalar, or a vector of length 10 (corresponding to a frame)% if the CFI varies per subframe. If CFI is scalar, create a local copy of% CFI as a vector (one value per subframe).ifnumel(enb.CFI) == 1 CFI = repmat(enb.CFI,1,10);elseCFI = enb.CFI;endforsnrIdx = 1:numel(SNRIn)% Comment out for parallel computing%parfor snrIdx = 1:numel(SNRIn) % Uncomment for parallel computing% Set the random number generator seed depending on the loop variable% to ensure independent random streamsrng(snrIdx,'combRecursive');% Reinitialize enb structures (they are modified during% each SNR point simulation)enb = enbInit;% Reinitialize muChannel and muPDSCH cell arraymuChannel = muChannelInit; muPDSCH = muPDSCHInit;% Initialize the state of all HARQ processesharqProcesses = cell(NUsers,1);foruserIdx = 1:NUsers harqProcesses{userIdx} = hNewHARQProcess(enb,muPDSCH{userIdx});endharqProcessSequence = 1;% Set up variables for the main looplastOffset = zeros(NUsers,1); frameOffset = zeros(NUsers,1); blkCRC = []; rxSymbols = cell(NUsers,2);% DL-SCH symbols for constellation plotbitTput = cell(NUsers,1); txedTrBlkSizes = cell(NUsers,1); W = cell(NUsers,1); pdschIndices = []; pdschRho = 0;% Flag to indicate if a precoding matrix cell array W is available.isWready = false;% Flag to indicate if a subframe is to be processed. Set to true if% there is data to be processed in the subframe, i.e. non-zero transport% block size.processSubframe = false;% Main for loop: for all subframesforsubframeNo = 0:(NFrames*10-1)% Update subframe numberenb.NSubframe = subframeNo;% Load CFI for current subframeenb.CFI = CFI(mod(subframeNo,length(CFI))+1);% Get HARQ process ID for the subframe from HARQ process sequenceharqID = harqProcessSequence(mod(subframeNo,...length(harqProcessSequence))+1);% Channel fading process time offset for the current subframe and% transport block size(s)trBlk = zeros(NUsers,2);% User can have maximum 2 transport blocktrBlkNext = zeros(NUsers,2);foruserIdx = 1:NUsers% Initialize channel fading process time offset for each subframemuChannel{userIdx}.InitTime = subframeNo/1000; trBlk(userIdx,1:ncw(userIdx)) = trBlkSizes{userIdx}(:,mod(subframeNo, 10)+1).';% Get transport block for next subframetrBlkNext(userIdx,1:ncw(userIdx))= trBlkSizes{userIdx}(:,mod(subframeNo+1,10)+1).';end% Set the flag to trigger subframe processingifisWready && any(trBlk(:)) processSubframe = true;elseprocessSubframe = false;end% Precoding matrix calculationifstrcmpi(enb.DuplexMode,'TDD')% Estimate channel in UL subframeifstrcmp(subframeType(mod(subframeNo,10)+1),'U') processSubframe = false;% UL subframe, no DL data% Only perform channel estimate if next subframe is DLifstrcmp(subframeType(mod((subframeNo+1),10)+1),'D') W = hMultiUserPrecodingMatrix(enb,muPDSCH,muChannel); isWready = true;endendelse%FDD% Calculate the precoding matrix for next subframe only if it% carries data (i.e. non-zero trBlkNext)ifany(trBlkNext(:)) W = hMultiUserPrecodingMatrix(enb,muPDSCH,muChannel); isWready = true;elseisWready = false;endend% Subframe processingifprocessSubframe% In this example, the variables pdschRho and pdschIndices will% have the same values for all userscodedTrBlk = zeros(NUsers,2);foruserIdx = 1:NUsers% Update current HARQ process for all usersharqProcesses{userIdx}(harqID) = hHARQScheduling(...harqProcesses{userIdx}(harqID), subframeNo, rvSequence);% Map precoding matrix of all users into PDSCH configuration% cell arraymuPDSCH{userIdx}.W = W{userIdx};% PDSCH resource element power allocation in dBpdschRho = muPDSCH{userIdx}.Rho;%生成指数佛r mapping of PDSCH symbols on resource% grid[pdschIndices,pdschInfo] = ltePDSCHIndices(enb,...muPDSCH{userIdx},muPDSCH{userIdx}.PRBSet);% Obtain coded transport block sizecodedTrBlk(userIdx,1:ncw(userIdx)) = pdschInfo.G;end% Generate grid without any PDSCH mapped[~,txGrid,enbOut] = lteRMCDLTool(enb,[]);% Get the HARQ ID sequence from 'enbOut' for HARQ processingharqProcessSequence = enbOut.PDSCH.HARQProcessSequence;% Generate complex-valued modulated symbol for PDSCH in multi-user% MIMO transmission with block-diagonalization precodingpdschSymbols = hMultiUserPDSCH(enb,muPDSCH,codedTrBlk,...harqProcesses,harqID); powerAdjPerRE = 10^(pdschRho/20);% Perform PDSCH symbols mapping on resource gridtxGrid(pdschIndices) = pdschSymbols*powerAdjPerRE;% Perform OFDM modulation[waveform,ofdmInfo] = lteOFDMModulate(enb,txGrid);% Add 25 sample padding. This is to cover the range of delays% expected from channel modeling (a combination of% implementation delay and channel delay spread)txWaveform = [waveform; zeros(25,NTxAnts)];% Calculate noise gain including compensation for downlink% power allocationSNR = 10^((SNRIn(snrIdx)-muPDSCH{userIdx}.Rho)/20);% Normalize noise power to take account of sampling rate,% which is a function of the IFFT size used in OFDM% modulation, and the number of antennasN0 = 1/(sqrt(2.0*NTxAnts*double(ofdmInfo.Nfft))*SNR);% Pass the waveform through noisy fading channels and receiver% operations for each userforuserIdx = 1:NUsers% Pass data through channel modelrxWaveform = lteFadingChannel(muChannel{userIdx},txWaveform);% Create additive white Gaussian noisenoise = N0*complex(randn(size(rxWaveform)),...randn(size(rxWaveform)));% Add AWGN to the received time domain waveformrxWaveform = rxWaveform + noise;% Receiver% Synchronization offset, OFDM demodulation,% perfect channel estimation, PDSCH and DL-SCH Decoding[harqProcesses{userIdx},dlschSymbols,lastOffset(userIdx)]...= hReceiverOperations(enb,muPDSCH{userIdx},rxWaveform,...muChannel{userIdx},harqProcesses{userIdx},trBlk(userIdx,...1:ncw(userIdx)),lastOffset(userIdx),harqID,subframeNo,noise);% Store the decoded DLSCH symbols for constellation% plottingrxSymbols{userIdx,1} = [rxSymbols{userIdx,1}; dlschSymbols{1}(:)];ifncw(userIdx)>1 rxSymbols{userIdx,2} = [rxSymbols{userIdx,2}; dlschSymbols{2}(:)];end% Store values to calculate throughput% Only for subframes with dataif(任何(trBlk (userIdx, 1:作战飞机(userIdx))) ~ = 0) blkCRC =[blkCRC harqProcesses{userIdx}(harqID).blkerr]; bitTput{userIdx} = [bitTput{userIdx}...trBlk(userIdx,1:ncw(userIdx)).*(1-harqProcesses{userIdx}(harqID).blkerr)]; txedTrBlkSizes{userIdx} = [txedTrBlkSizes{userIdx}...trBlk(userIdx,1:ncw(userIdx))];endendendend% Calculate the maximum and simulated throughputmaxTput = zeros(NUsers,1); simTput = zeros(NUsers,1);foruserIdx = 1:NUsers maxTput(userIdx) = sum(txedTrBlkSizes{userIdx});% Max possible throughputsimTput(userIdx) = sum(bitTput{userIdx},2);% Simulated throughput% Display the results dynamically in the command windowfprintf('\nSNR = %.2f dB. Throughput for UE-%d %d Frame(s) = %.4f Mbps\n',...SNRIn(snrIdx),userIdx,NFrames,1e-6*simTput(userIdx)/(NFrames*10e-3)); fprintf('SNR = %.2f dB. Throughput(%%) for UE-%d %d Frame(s) = %.4f %%\n',...SNRIn(snrIdx),userIdx, NFrames,simTput(userIdx)*100/maxTput(userIdx));endmaxThroughput(snrIdx,:) = maxTput; simThroughput(snrIdx,:) = simTput; rxConstellation(snrIdx,:,:)= rxSymbols;end% Plot received symbol constellationforsnrIdx = 1:numel(SNRIn) ii = 1; figure;foruserIdx = 1:NUsers subplot(NUsers,max(ncw),ii); plot(rxConstellation{snrIdx,userIdx,1},'.r'); title(['User 'num2str(userIdx)', Codeword 1, SNR '...num2str(SNRIn(snrIdx))' dB']); xlabel('In-Phase'); ylabel('Quadrature'); gridon;ifsize(rxConstellation{snrIdx,userIdx,2})~=0 ii = ii+1; subplot(NUsers,max(ncw),ii); plot(rxConstellation{snrIdx,userIdx,2},'.r'); title(['User 'num2str(userIdx)', Codeword 2, SNR '...num2str(SNRIn(snrIdx))' dB']); xlabel('In-Phase'); ylabel('Quadrature'); gridon;endii = ii+1;endend
SNR = 8.00 dB. Throughput for UE-1 2 Frame(s) = 0.0520 Mbps SNR = 8.00 dB. Throughput(%) for UE-1 2 Frame(s) = 26.3158 % SNR = 8.00 dB. Throughput for UE-2 2 Frame(s) = 0.1352 Mbps SNR = 8.00 dB. Throughput(%) for UE-2 2 Frame(s) = 68.4211 % SNR = 14.00 dB. Throughput for UE-1 2 Frame(s) = 0.1456 Mbps SNR = 14.00 dB. Throughput(%) for UE-1 2 Frame(s) = 73.6842 % SNR = 14.00 dB. Throughput for UE-2 2 Frame(s) = 0.1976 Mbps SNR = 14.00 dB. Throughput(%) for UE-2 2 Frame(s) = 100.0000 %

Throughput Results

The throughput results for all users are displayed in the MATLAB® command window after the simulation for each SNR point is completed. They are also captured in output arrayssimThroughputandmaxThroughput.

legendString = cell(NUsers,1); figure;foruserIdx = 1:NUsers plot(SNRIn, simThroughput(:,userIdx)*100./maxThroughput(:,userIdx),'*-.'); holdon; legendString{userIdx} = strcat('UE-',num2str(userIdx),': ',...num2str(muNumLayers(userIdx)),' layer(s), ',num2str(NTxAnts),...' TxAnt(s), ', num2str(muNumRxAnts(userIdx)),' RxAnt(s)');endgridon; xlabel('SNR (dB)'); ylabel('Throughput (%)'); legend(legendString,'Location','SouthEast');

For statistically valid results, the simulation should be run for a larger number of frames. The figure below shows the throughput results when simulating 1000 frames.

Appendix

This example uses the following helper functions:

选择Bibliography

  1. Spencer Q., A. Swindlehurst, M. Haardt. "Zero-Forcing Methods for Downlink Spatial Multiplexing in Multiuser MIMO Channels." IEEE Transactions on Signal Processing, Vol. 52, No. 2, February 2004, pp. 461-471.

  2. Lim C., T. Yoo, B. Clerckx, B. Lee, B. Shim. "Recent trend of multi-user MIMO in LTE-advanced." IEEE Communications Magazine, March 2013, pp. 127-135.