毫米波MIMO系统中的空间稀疏预编码附matlab代码_大规模mimo ofdm 混合预编码 matlab-优快云博客

本文链接：https://blog.youkuaiyun.com/matlab_dingdang/article/details/130313578

本文探讨了大规模多输入多输出(MIMO)系统中预编码算法的三种类型——数字、模拟和混合预编码，分析了它们的优缺点和适用场景。同时，对信道估计方案进行了训练估计和盲估计的比较，并强调利用信道稀疏性可以改进估计效果。通过Matlab仿真展示了不同预编码策略在不同SNR下的性能比较。

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⛄ 内容介绍

大规模多输入多输出(MIMO)技术能够大幅度提升系统容量,降低不同用户间的干扰,但因其系统中信道维度高、信道估计和预编码算法复杂等因素,使得系统软硬件开销都会增大。将大规模MIMO系统的预编码算法分为数字、模拟和混合3种类型,并对3类预编码算法进行了归纳对比,总结出不同预编码算法的优缺点和适用场景。将信道估计方案分为训练估计和盲估计,归纳总结了2类方案的优缺点,并指出合理利用大规模MIMO的信道稀疏性能够改善信道估计的质量,减少估计开销。

⛄ 部分代码

clear all

close all

snrValuesDB = -40:5:0; %in Figures

snrValues = 10.^(snrValuesDB./10);

tryNumber = 100;

%% Fig4 Parameters

parameters_Fig4 = containers.Map('KeyType','char','ValueType','any');

parameters_Fig4('numberTransmitAntennas') = 256; % Number of transmit antennas

parameters_Fig4('numberRecieveAntennas') = 64; % Number of receive antennas

parameters_Fig4('numberDataStreams') = 1; % Number of data streams

parameters_Fig4('numberRFChains') = 6; % Number of RF chains for precoding and combining

parameters_Fig4('numberCluster') = 8; % Number of clusters

parameters_Fig4('numberRayPerCluster') = 10; % Number of rays per cluster

parameters_Fig4('angularSpread') = 7.5; % Angular spread of 7.5 degree

spectralEffOptimal = zeros(tryNumber,length(snrValues));

spectralEffHybrid = zeros(tryNumber,length(snrValues));

spectralEffBeam = zeros(tryNumber,length(snrValues));

for s = 1:length(snrValues)

SNR = snrValues(s);

for i = 1:tryNumber

channel = ChannelGeneration(parameters_Fig4);

tempObj = OptimalUnconstraint(SNR,channel);

spectralEffOptimal(i,s) = tempObj.spectralEfficiency;

tempObj = HybridSparsePrecoding(SNR,channel);

spectralEffHybrid(i,s) = tempObj.spectralEfficiency;

tempObj = BeamSteering(SNR,channel);

spectralEffBeam(i,s) = tempObj.spectralEfficiency;

end

% Averaging Tries

spectralEffOptimalSNR = mean(spectralEffOptimal,1);

spectralEffHybridSNR = mean(spectralEffHybrid,1);

spectralEffBeamSNR = mean(spectralEffBeam,1);

% figure();

hold on

l1 = plot(snrValuesDB,spectralEffOptimalSNR,'-s','Color',[0 0.5 0],'LineWidth',2.0,'MarkerSize',8.0);

l2 = plot(snrValuesDB,spectralEffHybridSNR,'-o','Color',[0 0.45 0.74],'LineWidth',2.0,'MarkerSize',8.0);hold on;

l3 = plot(snrValuesDB,spectralEffBeamSNR,'-d','Color',[0.85 0.33 0.1],'LineWidth',2.0,'MarkerSize',8.0);hold on;

%% Data stream

parameters_Fig4('numberDataStreams') = 2; % Number of data streams

spectralEffOptimal = zeros(tryNumber,length(snrValues));

spectralEffHybrid = zeros(tryNumber,length(snrValues));

for s = 1:length(snrValues)

SNR = snrValues(s);

for i = 1:tryNumber

channel = ChannelGeneration(parameters_Fig4);

tempObj = OptimalUnconstraint(SNR,channel);

spectralEffOptimal(i,s) = tempObj.spectralEfficiency;

tempObj = HybridSparsePrecoding(SNR,channel);

spectralEffHybrid(i,s) = tempObj.spectralEfficiency;

end

% Averaging Tries

spectralEffOptimalSNR = mean(spectralEffOptimal,1);

spectralEffHybridSNR = mean(spectralEffHybrid,1);

spectralEffBeamSNR = mean(spectralEffBeam,1);

l4 = plot(snrValuesDB,spectralEffOptimalSNR,'-s','LineWidth',2.0,'MarkerSize',8.0);

l5 = plot(snrValuesDB,spectralEffHybridSNR,'-o','LineWidth',2.0,'MarkerSize',8.0);hold on;

legend([l1 l2 l3 l4 l5],'Optimal unconstrained precoding N_s = 1','Hybrid precoding and combining N_s = 1','Beam steering N_s = 1','Optimal unconstrained precoding N_s = 2','Hybrid precoding and combining N_s = 2','Location','northwest','FontSize', 15);

xlabel('SNR (dB)','FontSize', 20)

ylabel('Spectral Efficiency(bits/s/Hz)','FontSize', 20)