一、深度学习OFDM系统信号检测简介

1 OFDM系统模型
OFDM系统是一种常用的多载波调制方式, 因为其具有能够在带宽较窄的情况下传输大量数据、能够对抗频率选择性衰落、传输速率高、实现简单、信道利用率高、抗窄带干扰能力强等优点而受到广泛使用。本节主要介绍了OFDM系统实现的基本原理以及系统模型。

1.1OFDM系统基本原理
因为频谱资源十分珍贵,如果一个信道仅仅只传输一路信号,那无疑是对频谱资源的浪费, 为了能够充分利用信道带宽, 频分复用是一种很好的方法。OFDM技术是频分复用方法的一种,其基本原理如下:将串行的高速数据流转换为低速的并行数据流,然后将数据调制到相互正交的子载波上4Q。OFDM系统可以通过DFT和ID FT的方法产生相互正交的子载波,然后利用子载波之间的正交性,将原始信号从子载波中分离开来,通过这种方法可以实现OFDM无线通信系统中信号的发送与接收。

1.2OFDM系统模型

OFDM符号是由若干个子载波组成, 且每个子载波都要受到相移键控I+!!(phase-shift keying, PSK) 或者正交幅度调制+2(Quadrature Amplitude Modulation, QAM) 的调制。在发送端, 设N为子载波的数量, T为OFDM符号的宽度, d(i=0, 1…N-1)为分配给每个子载波的数据符号, f:为第0个子载波的载波频率, rect() =1, |sT/2,则从t=1, 开始的OFDM符号表达式为:

【OFDM通信】基于matlab深度学习OFDM系统信号检测【含Matlab源码 2023期】_时域


【OFDM通信】基于matlab深度学习OFDM系统信号检测【含Matlab源码 2023期】_深度学习_02


但是使用离散傅里叶变换也有其缺点, 因为随着OFDM无线通信系统的子载波数目的增多,离散傅里叶变换的运算复杂度也会呈指数倍增长,因此在实际工程中常常使用FFT/IF FT代替离散傅里叶变换, 这样可以很大程度上减少运算的时间和占用的硬件资源,同时也可以降低数据传输时延。

【OFDM通信】基于matlab深度学习OFDM系统信号检测【含Matlab源码 2023期】_matlab_03


由图2.2所示, 信源发出比特流信号, 比特流信号需要经过PSK或者QAM调制,然后经过串并转换的同时加入导频信号后被分成M路数据,M路数据经过快速傅里叶逆变换后转换为N个时域离散信号, 插入循环前缀(Cyclic Prefix, CP) 是为了避免ICI对整个系统的影响,其具体过程是取N个时域信号的后L,位数据放在该符号的前面形成循环结构, 这样就能保证即使是有时延的OFDM符号在经过FFT后依然是整数倍周期。最后将插入CP之后的OFDM符号经过并串转换之后就可以通过发射机发送出去。接收机接收到信号之后先把数据进行串并转换,然后按照按照插入CP的方式移除CP,CP的长度应该大于信道的时延, 只有这样才能消除符号间干扰, 然后对信号进行FFT变换,将信号从时域转换到频域,接收信号可以表示为

Y=XH+Z(2-8)

式中:Y为接收信号的频域形式,x为发送信号的频域形式,H为信道冲激响应,z为噪声。

二、部分源代码

%% 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
function ReceivedPacket = genTransmissionReceptionOFDM(TransmittedFrame,LengthCP,h,NoiseVar)
% This function is to model the transmission and reception process in OFDM systems.% Extract parameters
[NumSym,NumSC,NumPacket] = size(TransmittedFrame);%% Transmitter
PhaseShift = exp(-1j*rand(1,NumPacket)2pi);
for p = 1:NumPacket
% 1. IFFT
x1 = ifft(TransmittedFrame(:,:,p),NumSC,2);

% 2. Inserting CP
x1_CP = [x1(:,NumSC-LengthCP+1:end) x1];

% 3. Parallel to serial transformation
x2 = x1_CP.';
x = x2(:);

% 4. Channel filtering
y_conv = conv(h*PhaseShift(p),x);
y(:,p) = y_conv(1:length(x));
end
%% Adding noise
SeqLength = size(y,1);
% Calculate random noise in time domain
NoiseF = sqrt(NoiseVar)/sqrt(2).(randn(NumPacket,NumSC)+1jrandn(NumPacket,NumSC)); % Frequency-domain noise
NoiseT = sqrt(SeqLength)*sqrt(SeqLength/NumSC)*ifft(NoiseF,SeqLength,2); % Time-domain noise% Adding noise
y = y+NoiseT.';%% Receiver
ReceivedPacket = zeros(NumPacket,NumSym,NumSC);
for p = 1:NumPacket
% 1. Serial to parallem transformation
y1 = reshape(y(:,p),NumSC+LengthCP,NumSym).';

% 2. Removing CP
y2 = y1(:,LengthCP+1:LengthCP+NumSC);

% 3. FFT, # x NymSym x 64
ReceivedPacket(p,:,:) = fft(y2,NumSC,2); % NumSym x 64

end

ReceivedPacket = permute(ReceivedPacket,[2,3,1]);

三、运行结果

【OFDM通信】基于matlab深度学习OFDM系统信号检测【含Matlab源码 2023期】_开发语言_04

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 高俊伟.基于深度学习的OFDM信号检测算法研究[D].哈尔滨工程大学

3 备注
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