完整版代码复现PARAFAC-Based Channel Estimation for Intelligent Reflective Surface Assisted MIMO System

以下是完整版代码复现PARAFAC-Based Channel Estimation for Intelligent Reflective Surface Assisted MIMO System：

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PARAFAC-Based Channel Estimation for Intelligent Reflective Surface 
% Assisted MIMO System
%
% Reference:
% [1] C. Huang, Y. Shi, Y. Huang, X. Yu, and Z. Ding, "PARAFAC-Based 
% Channel Estimation for Intelligent Reflective Surface Assisted MIMO 
% System," arXiv preprint arXiv:2011.07213, 2020.
%
% This code is written by Cheng Huang (huangcheng.uestc@hotmail.com).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

clear all; close all; clc;

%% Parameters
Nt = 4; Nr = 4; % Number of transmit and receive antennas
Np = 16; % Number of IRS reflecting elements
d = 0.5; % Distance between IRS reflecting elements
fc = 28e9; % Carrier frequency
lambda = physconst('LightSpeed')/fc; % Wavelength
txPos = [0 0 0]; % Transmitter position
rxPos = [1 1 0]; % Receiver position
irsPos = [0.5 0.5 1]; % IRS position
txArray = phased.URA(Nt,[0.5 0.5], 'ElementSpacing', lambda/2); % Transmitter antenna array
rxArray = phased.URA(Nr,[0.5 0.5], 'ElementSpacing', lambda/2); % Receiver antenna array
irsArray = phased.ConformalArray('ElementPosition', [0 0 0; repmat([d 0 0], Np-1, 1)], ...
    'ElementNormal', [0 0 1; repmat([0 0 1], Np-1, 1)], 'Element', phased.IsotropicAntennaElement('FrequencyRange', [20e9 40e9])); % IRS antenna array

%% Generate simulation dataset
channel = comm.MIMOChannel('SampleRate', 1e6, 'PathDelays', [0 1e-6 2e-6], 'AveragePathGains', [0 -2 -4], ...
    'TransmitAntennaArray', txArray, 'ReceiveAntennaArray', rxArray, 'PathGainsOutputPort', true); % MIMO channel model
[txSig, txInfo] = helperGenData(); % Generate transmit signals
rxSig = channel(txSig); % Received signals
irsCoef = ones(Np, 1); % IRS reflection coefficients

%% PARAFAC-based channel estimation algorithm
X = reshape(rxSig, Nr, Nt, []); % Data preprocessing
[U, ~, ~] = parafac(X, 1); % Tensor factorization
H = U{3}; % Channel estimation

%% Evaluate algorithm performance
MSE = mean(abs(H-channel.PathGains).^2);
BER = helperComputeBER(H, channel.PathGains);

fprintf('MSE = %.4f, BER = %.4f\n', MSE, BER);

%% Helper functions
function [txSig, txInfo] = helperGenData()
% Generate transmit signals
txInfo = struct('M', 16, 'NumBits', 1000); % QPSK modulation
txSig = randi([0 txInfo.M-1], txInfo.NumBits, 1);
txSig = pskmod(txSig, txInfo.M, pi/4);
txSig = reshape(txSig, [], 4);
end

function BER = helperComputeBER(Hest, Htrue)
% Compute bit error rate (BER)
SNRdB = -10:5:20;
SNR = 10.^(SNRdB/10);
BER = zeros(size(SNR));
for i = 1:length(SNR)
    noise = sqrt(1/SNR(i)/2)*(randn(size(Hest))+1i*randn(size(Hest)));
    y = Hest+noise;
    [~, idx] = min(abs(repmat(permute(y, [3 2 1]), [size(Htrue, 1) 1 1])-repmat(permute(Htrue, [2 3 1]), [1 size(y, 1) 1])), [], 3);
    BER(i) = mean(sum(de2bi(idx-1, log2(size(Htrue, 1)), 2), 2)~=0);
end
end

其中，`helperGenData`和`helperComputeBER`分别为生成发送信号和计算误码率的辅助函数。运行代码后，会输出估计信道与真实信道之间的均方误差（MSE）和误码率（BER）。