Table of contents
1. The principle of massive MIMO
2. Throughput performance of massive MIMO
3. Channel Capacity of Massive MIMO
Massive multiple-input multiple-output (Massive MIMO) is a key technology of the fifth generation mobile communication system (5G), which aims to significantly improve system throughput, spectral efficiency and connection reliability. It achieves highly parallel data transmission by using a large number of antennas at the base station to simultaneously serve multiple users.
1. The principle of massive MIMO
Massive MIMO utilizes a large number of antennas to realize parallel data transmission between multiple users through spatial division multiplexing (Spatial Division Multiplexing, SDM). The base station exploits spatial diversity to increase channel capacity by allocating data streams to different antennas. In addition, massive MIMO also uses beamforming technology to reduce interference and improve signal quality and system capacity.
Massive MIMO (Massive MIMO) technology is a technology that uses a large number of antennas for signal transmission and reception, which can improve the capacity, rate and reliability of wireless communication systems. Massive MIMO technology is an extension of MIMO technology. By increasing the number of antennas and using higher frequencies, the performance of the communication system can be further improved. Massive MIMO technology usually requires the use of hundreds or even thousands of antennas, which can serve multiple users at the same time and improve the capacity and spectrum efficiency of the communication system. Massive MIMO technology can also use technologies such as spatial diversity and time division multiplexing to transmit multiple signals at the same time, thereby improving the capacity and rate of the communication system.
2. Throughput performance of massive MIMO
The throughput performance of massive MIMO is affected by many factors, including the number of antennas, number of users, channel state, noise, etc. Its throughput performance can be expressed by the following formula:
Throughput (Throughput) = number of users × single user throughput
Among them, single-user throughput depends on channel capacity, signal-to-noise ratio (SNR), and modulation and coding schemes. At a high SNR, the channel capacity is close to the theoretical upper limit, that is, the Shannon Capacity, which can be calculated by the formula:
Shannon Capacity = Bandwidth × log2(1 + SNR)
3. Channel Capacity of Massive MIMO
The channel capacity in massive MIMO is affected by the increase of spatial degrees of freedom, and its channel capacity can be estimated by the following formula:
Channel capacity = min(number of antennas, number of users) × log2(1 + SNR × spatial degrees of freedom)
Among them, the spatial degree of freedom represents the number of independent data streams that can be transmitted in a given channel state, which is related to the number of antennas and the number of users.
4. Advantages of massive MIMO
High throughput: Massive MIMO significantly increases the overall system throughput by providing high-speed data transmission to multiple users simultaneously.
Spectrum efficiency: Through spatial multiplexing and beamforming, massive MIMO can support more users with limited spectrum resources.
Anti-interference ability: Massive MIMO uses beamforming technology to reduce interference and improve signal quality and system capacity.
Coverage Extension: Massive MIMO can also extend network coverage by redirecting signals to specific areas through beamforming.
The massive MIMO technology based on the 5G communication system is excellent in terms of throughput performance. It achieves high-speed data transmission, improved spectral efficiency, and strong anti-interference capabilities by utilizing a large number of antennas, spatial degrees of freedom, and beamforming. This makes massive MIMO an important technology in 5G networks and provides strong support for the development of future mobile communication systems.
5. Some core programs
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%%仿真参数
rng('default');
rng('shuffle'); %使用随机种子启动随机数生成器
%%如果rng（“shuffle”）；您的Matlab版本不支持，您可以使用
%%而是使用以下命令：
%randn（“状态”，sum（100*时钟））；
Mantennas = 10:10:100;%BS天线数量范围
%用户数量
K = 10;
%是否应该计算最佳波束成形？（真/假）
%（注意，它是计算最优波束形成的主要来源
%计算复杂性。最优波束形成的运行时间
computeCapacity = false;
%蒙特卡罗模拟中的实现次数
monteCarloRealizations = 100;
%组合信道矩阵为（K x K*M）。此矩阵给出
%每个信道元素的归一化方差
channelVariances = ones(1,K);
%用于（未加权的）和速率计算的用户权重
weights = ones(K,1);
%SNR值范围
SNRdB = 5; %dB刻度
SNR = K*10.^(SNRdB/10); %线性刻度
%矩阵的预分配
%不同波束形成状态下节省和速率的矩阵
sumRateZF = zeros(monteCarloRealizations,length(Mantennas));
sumRateFP = zeros(monteCarloRealizations,length(Mantennas));
sumrateCAPACITY = zeros(monteCarloRealizations,length(Mantennas));
%浏览不同数量的基站天线
for n = 1:length(Mantennas)
%提取当前天线数量
M = Mantennas(n);
%瑞利衰落信道实现的预生成（单位方差）
Hall = (randn(K,M,monteCarloRealizations)+1i*randn(K,M,monteCarloRealizations))/sqrt(2);
%输出进度
disp(['Progress: M = ' num2str(M)]);
%完成所有通道实现
for m = 1:monteCarloRealizations
%生成第m个实现的信道矩阵
H = repmat(sqrt(channelVariances)',[1 M]) .* Hall(:,:,m);
%计算MR的归一化波束成形矢量
wMRT = functionMRT(H);
%计算ZF的归一化波束成形矢量
wZF = functionZFBF(H);
%用MR计算功率分配（使用[3]中的定理3.5）
rhos = diag(abs(H*wMRT).^2)';
powerAllocationFP = functionHeuristicPowerAllocation(rhos,SNR,weights);
%计算无干扰的和速率（通过消除干扰
W = kron(sqrt(powerAllocationFP),ones(M,1)).*wMRT;
channelGains = abs(H*W).^2;
signalGains = diag(channelGains);
rates = log2(1+signalGains);
sumRateFP(m,n) = weights'*rates;
%%用ZF计算功率分配（使用[3]中的定理3.5）
rhos = diag(abs(H*wZF).^2)';
powerAllocationwZFBF = functionHeuristicPowerAllocation(rhos,SNR,weights);
%使用ZFBF计算总和比率
W = kron(sqrt(powerAllocationwZFBF),ones(M,1)).*wZF;
channelGains = abs(H*W).^2;
signalGains = diag(channelGains);
interferenceGains = sum(channelGains,2)-signalGains;
rates = log2(1+signalGains./(interferenceGains+1));
sumRateZF(m,n) = weights'*rates;
%使用容量计算总和速率
if computeCapacity == true
sumrateCAPACITY(m,n) = real(function_capacity_broadcast(H,K,SNR));
end
end
end
%绘图模拟结果
figure; hold on; box on;
plot(Mantennas,mean(sumRateFP,1),'k--','LineWidth',1);
plot(Mantennas,mean(sumrateCAPACITY,1),'ro-','LineWidth',1);
plot(Mantennas,mean(sumRateZF,1),'b*--','LineWidth',1);
legend('无干扰','非线性：总和容量','线性: ZF','位置','东南');
xlabel('BS天线数量 (M) ');
ylabel('频谱效率 (bit/s/Hz/cell)');
ylim([0 90]);
21956. Simulation results
