1.完整项目描述和程序获取
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2.部分仿真图预览
3.算法概述
信道估计器是接收机一个很重要的组成部分。在OFDM系统中，信道估计器的设计上要有两个问题:一是导频信息的选择，由于无线信道的时变特性，需要接收机不断对信道进行跟踪，因此导频信息也必须不断的传送: 二是既有较低的复杂度又有良好的导频跟踪能力的信道估计器的设计，在确定导频发送方式和信道估计准则条件下，寻找最佳的信道估计器结构。 **在实际设计中，导频信息的选择和最佳估计器的设计通常又是相互关联的，因为估计器的性能与导频信息的传输方式有关。正交频分复用(orthogonalfrequencydivisionmultiplexing，ofdm)调制因其频带利用率高、抗频率选择性衰落的性能在通信系统中得到了广泛的应用，但ofdm解调对信道估计、时频偏估计、采样起始时刻偏差的估计精度要求很高，尤其是当使用高阶调制时，这一问题更为明显。信道估计和均衡是ofdm系统设计中的关键技术之一，通常ofdm系统的信道估计方法分为非盲信道估计方法和盲信道估计方法。非盲信道估计方法通常采用插入训练序列的方式或者插入导频的方式，盲信道估计方法则是在接收端采用相应的信号处理技术来获得信道时域或频域响应的估计值，有基于判决反馈的估计方法和基于相位的信道估计方法。
4.部分源码
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for i = 1:NumIter
for snr = 1:length(NoiseVar)
[i,snr]
% 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
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