m基于深度学习的16QAM调制解调系统频偏估计和补偿算法matlab仿真
1.算法仿真效果
matlab2022a仿真结果如下:
2.算法涉及理论知识概要
在通信系统中,载波频率偏差(频偏)会导致接收信号与本地参考载波之间的相位差变化,严重影响调制信号的正确解调。对于16-QAM这类高级调制方案而言,频偏补偿至关重要。深度学习技术能够通过学习信号特性来实现精准的频偏估计。
上述简化了频偏估计和补偿的实际操作,真实的深度学习模型可能会更复杂,并且会考虑到噪声、多径效应等因素的影响。此外,实际应用中可能会利用卷积神经网络结合循环神经网络(RNN)或是长短时记忆网络(LSTM)等架构,以捕捉信号的时间动态特性。
3.MATLAB核心程序
global Fs; global LEN; global K; K = 4; %调制阶数 SNR = [0:2:30]; %信噪比范围0~30 OFFSET = 6;%频偏范围0~10Hz LEN = 2000; Fs = 1e4; t = [1:LEN/K]/Fs; for i = 1:length(SNR) i for j = 1:10 [i,j] %产生信号 signal = round(rand(1,LEN)); signal_modulated1 = Modulator(signal,K); signal_receive1 = awgn(signal_modulated1,SNR(i),'measured'); signal_receive2 = signal_receive1.*exp(sqrt(-1)*2*pi*OFFSET*t ); offset2 = func_phase_est_dnn(signal_receive2);%基于深度学习的相位估计 RR = signal_receive2.*exp(-sqrt(-1)*2*pi*mean2(offset2)*t); %加相位补偿 output = DeModulator(RR,K); msgr = ones(size(output)); idx = find(output<=0); msgr(idx) = 0; len = length(find(signal==msgr)); errrate(i,j)= 1-len/length(signal); %没有相位补偿 output2 = DeModulator(signal_receive2,K); msgr2 = ones(size(output2)); idx2 = find(output2<=0); msgr2(idx2) = 0; len2 = length(find(signal==msgr2)); errrate2(i,j)= 1-len2/length(signal); end end figure; semilogy(SNR,mean(errrate2,2),'b-o'); hold on semilogy(SNR,mean(errrate,2),'r-s'); grid on xlabel('SNR'); ylabel('误码率'); legend('16QAM无频偏补偿误码率','16QAM频偏补偿误码率');