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⛄ 内容介绍
在满足未来5G网络的容量要求方面，毫米波（mmWave）通信系统引起了极大的兴趣。毫米波系统的频率范围在30到300GHz之间，总共有大约250GHz的带宽可用。与微波信号相比，它们的路径损耗要多出几个数量级。幸运的是，毫米波频率的小波长使大量的天线元件可以部署在相同的外形尺寸中，从而提供高空间处理收益。带有多个数据流的波束成形，即所谓的预编码，可用于进一步提高毫米波的频谱效率。由于对高成本和复杂性的关注，对射频处理的关注有所增加。关于硬件限制的解决方案投射到双方的预编码和组合器的可行集上。在本文中，我们考虑了大型天线阵列和设计这种符合实际限制的系统。我们将该问题表述为稀疏重建问题，并使用众所周知的匹配追求算法来寻找接近最优无约束解决方案的解决方案。我们介绍了在中等和大型天线阵列、不同角度传播和集群环境中的性能。我们将我们的发现与波束转向方案和最佳无约束方案进行了比较。
⛄ 部分代码
clear all
snrValuesDB = -40:5:0; %in Figures
snrValues = 10.^(snrValuesDB./10);
tryNumber = 100;
%% Fig3 Parameters
parameters_Fig3 = containers.Map('KeyType','char','ValueType','any');
parameters_Fig3("numberTransmitAntennas") = 64; % Number of transmit antennas
parameters_Fig3("numberRecieveAntennas") = 16; % Number of receive antennas
parameters_Fig3("numberDataStreams") = 1; % Number of data streams
parameters_Fig3("numberRFChains") = 4; % Number of RF chains for precoding and combining
parameters_Fig3("numberCluster") = 8; % Number of clusters
parameters_Fig3("numberRayPerCluster") = 10; % Number of rays per cluster
parameters_Fig3("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_Fig3);
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
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_Fig3("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_Fig3);
tempObj = OptimalUnconstraint(SNR,channel);
spectralEffOptimal(i,s) = tempObj.spectralEfficiency;
tempObj = HybridSparsePrecoding(SNR,channel);
spectralEffHybrid(i,s) = tempObj.spectralEfficiency;
end
end
% Averaging Tries
spectralEffOptimalSNR = mean(spectralEffOptimal,1);
spectralEffHybridSNR = mean(spectralEffHybrid,1);
spectralEffBeamSNR = mean(spectralEffBeam,1);
% figure();
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)
⛄ 运行结果
⛄ 参考文献
O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi and R. W. Heath, "Spatially Sparse Precoding in Millimeter Wave MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp. 1499-1513, March 2014, doi: 10.1109/TWC.2014.011714.130846.
