【OFDM通信】基于深度学习的OFDM系统信号检测附matlab代码.zip

%% Testing % % This script % 1. generates testing data for each SNR point; % 2. calculates the symbol error rate (SER) based on deep learning (DL), % least square (LS) and minimum mean square error (MMSE). %% Clear workspace clear variables; close all; %% Load common parameters and the trained NN load('SimParametersPilot64.mat'); load('TrainedNetPilot64.mat'); %% Other simulation parameters NumPilot = length(FixedPilot); PilotSpacing = NumSC/NumPilot; NumOFDMsym = NumPilotSym+NumDataSym; NumClass = length(Label); NumPath = length(h); % Load pre-calculated channel autocorrelation matrix for MMSE estimation % This autocorrelation matrix is calculated in advance using the 3GPP % channel model, which can be replaced accordingly. load('RHH.mat'); %% SNR range Es_N0_dB = 0:2:20; % Es/N0 in dB Es_N0 = 10.^(Es_N0_dB./10); % linear Es/N0 N0 = 1./Es_N0; NoiseVar = N0./2; %% Testing data size NumPacket = 10000; % Number of packets simulated per iteration %% Simulation % Same pilot sequences used in training and testing stages FixedPilotAll = repmat(FixedPilot,1,1,NumPacket); % Number of Monte-Carlo iterations NumIter = 1; % Initialize error rate vectors SER_DL = zeros(length(NoiseVar),NumIter); SER_LS = zeros(length(NoiseVar),NumIter); SER_MMSE = zeros(length(NoiseVar),NumIter); for i = 1:NumIter for snr = 1:length(NoiseVar) %% 1. Testing data generation noiseVar = NoiseVar(snr); % OFDM pilot symbol (can be interleaved with random data symbols) PilotSym = 1/sqrt(2)*complex(sign(rand(NumPilotSym,NumSC,NumPacket)-0.5),sign(rand(NumPilotSym,NumSC,NumPacket)-0.5)); PilotSym(1:PilotSpacing:end) = FixedPilotAll; % OFDM data symbol DataSym = 1/sqrt(2)*complex(sign(rand(NumDataSym,NumSC,NumPacket)-0.5),sign(rand(NumDataSym,NumSC,NumPacket)-0.5)); % Transmitted OFDM frame TransmittedPacket = [PilotSym;DataSym]; % Received OFDM frame ReceivedPacket = genTransmissionReceptionOFDM(TransmittedPacket,LengthCP,h,noiseVar); % Collect the data labels for the selected subcarrier DataLabel = zeros(size(DataSym(:,idxSC,:))); for c = 1:NumClass DataLabel(logical(DataSym(:,idxSC,:) == 1/sqrt(2)*Mod_Constellation(c))) = Label(c); end DataLabel = squeeze(DataLabel); % Testing data collection XTest = cell(NumPacket,1); YTest = zeros(NumPacket,1); for c = 1:NumClass [feature,label,idx] = getFeatureAndLabel(real(ReceivedPacket),imag(ReceivedPacket),DataLabel,Label(c)); featureVec = mat2cell(feature,size(feature,1),ones(1,size(feature,2))); XTest(idx) = featureVec; YTest(idx) = label; end YTest = categorical(YTest); %% 2. DL detection YPred = classify(Net,XTest,'MiniBatchSize',MiniBatchSize); SER_DL(snr,i) = 1-sum(YPred == YTest)/NumPacket; %% 3. LS & MMSE detection % Channel estimation wrapper = @(x,y) performChanEstimation(x,y,RHH,noiseVar,NumPilot,NumSC,NumPath,idxSC); ReceivedPilot = mat2cell(ReceivedPacket(1,:,:),1,NumSC,ones(1,NumPacket)); PilotSeq = mat2cell(FixedPilotAll,1,NumPilot,ones(1,NumPacket)); [EstChanLS,EstChanMMSE] = cellfun(wrapper,ReceivedPilot,PilotSeq,'UniformOutput',false); EstChanLS = cell2mat(squeeze(EstChanLS)); EstChanMMSE = cell2mat(squeeze(EstChanMMSE)); % Symbol detection SER_LS(snr,i) = getSymbolDetection(ReceivedPacket(2,idxSC,:),EstChanLS,Mod_Constellation,Label,DataLabel); SER_MMSE(snr,i) = getSymbolDetection(ReceivedPacket(2,idxSC,:),EstChanMMSE,Mod_Constellation,Label,DataLabel); end end SER_DL = mean(SER_DL,2).'; SER_LS = mean(SER_LS,2).'; SER_MMSE = mean(SER_MMSE,2).'; figure(); semilogy(Es_N0_dB,SER_DL,'r-o','LineWidth',2,'MarkerSize',10);hold on; semilogy(Es_N0_dB,SER_LS,'b-o','LineWidth',2,'MarkerSize',10);hold on; semilogy(Es_N0_dB,SER_MMSE,'k-o','LineWidth',2,'MarkerSize',10);hold off; legend('Deep learning (DL)','Least square (LS)','Minimum mean square error (MMSE)'); xlabel('Es/N0 (dB)'); ylabel('Symbol error rate (SER)'); %% function [EstChanLS,EstChanMMSE] = performChanEstimation(ReceivedData,PilotSeq,RHH,NoiseVar,NumPilot,NumSC,NumPath,idxSC) % This function is to perform LS and MMSE channel estimations using pilot % symbols, second-order statistics of the channel and noise variance [1]. % [1] O. Edfors, M. Sandell, J. -. van de Beek, S. K. Wilson and % P. Ola Borjesson, "OFDM channel estimation by singular value % decomposition," VTC, Atlanta, GA, USA, 1996, pp. 923-927 vol.2. PilotSpacing = NumSC/NumPilot; %%%%%%%%%%%%%%% LS estimation with interpolation %%%%%%%%%%%%%%%%%%%%%%%%% H_LS = ReceivedData(1:PilotSpacing:NumSC)./PilotSeq; H_LS_interp = interp1(1:PilotSpacing:NumSC,H_LS,1:NumSC,'linear','extrap'); H_LS_interp = H_LS_interp.'; %%%%%%%%%%%%%%%% MMSE estimation based on LS %%%%%%%%%%%%%%%% [U,D,V] = svd(RHH); d = diag(D); InvertValue = zeros(NumSC,1); if NumPilot >= NumPath InvertValue(1:NumPilot) = d(1:NumPilot)./(d(1:NumPilot)+NoiseVar); else InvertValue(1:NumPath) = d(1:NumPath)./(d(1:NumPath)+NoiseVar); end H_MMSE = U*diag(InvertValue)*V'*H_LS_interp; %%%%%%%%%%%%%%% Channel coefficient on the selected subcarrier %%%%%%%%%%% EstChanLS = H_LS_interp(idxSC); EstChanMMSE = H_MMSE(idxSC); end %% function SER = getSymbolDetection(ReceivedData,EstChan,Mod_Constellation,Label,DataLabel) % This function is to calculate the symbol error rate from the equalized % symbols based on hard desicion. EstSym = squeeze(ReceivedData)./EstChan; % Hard decision DecSym = sign(real(EstSym))+1j*sign(imag(EstSym)); DecLabel = zeros(size(DecSym)); for c = 1:length(Mod_Constellation) DecLabel(logical(DecSym == Mod_Constellation(c))) = Label(c); end SER = 1-sum(DecLabel == DataLabel)/length(EstSym); end