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简介
此存储库包含无线通信系统中使用的各种信道估计技术。重点是这些技术的实施和分析,通过有效估计信道来提高数据传输的准确性和效率。
信道估计是无线通信中的一个关键过程,它使接收器能够估计信道的状态并减轻其对传输信号的影响。此存储库提供:
- 流行的通道估计技术的实现。
- 这些技术在不同条件下的性能比较分析。
- 用于了解信道估计的理论和应用的文档和资源。
涵盖的技术
- 最小二乘法 (LS) 估计
- 最小均方误差 (MMSE) 估计
- 贝叶斯通道估计
- 基于深度学习的信道估计
有用的链接
- Logistic 回归模型的贝叶斯分析
- https://in.mathworks.com/help/stats/bayesian-analysis-for-a-logistic-regression-model.html
- 在 Google Colab 中使用 Sionna 进行 OFDM MIMO 检测
- https://colab.research.google.com/github/nvlabs/sionna/blob/main/examples/OFDM_MIMO_Detection.ipynb
- 用于 5G 信道估计的深度学习数据合成
- https://www.mathworks.com/help/5g/ug/deep-learning-data-synthesis-for-5g-channel-estimation.html
代码展示
clc; clear; N = 1000; % Number of symbols x = randi([0 1], 1, 2*N); % QPSK Modulation (Baseband) xmod = ((1-2*x(1:2:end)) + 1j * (1-2*x(2:2:end))) / sqrt(2); % Alamouti STBC Encoding s1 = xmod(1:2:end); s2 = xmod(2:2:end); s = [s1; s2]; s = [s; [-conj(s(2,:)); conj(s(1,:))]]; % Channel: Assume two independent channels (MISO) h1 = (randn(1, N/2) + 1j * randn(1, N/2)) / sqrt(2); h2 = (randn(1, N/2) + 1j * randn(1, N/2)) / sqrt(2); % Noise generation noise = (randn(2, N/2) + 1j * randn(2, N/2)) / sqrt(2); snr_db = 0:2:20; % SNR values in dB ber = zeros(size(snr_db)); % To store BER values for i = 1:length(snr_db) snrdb = snr_db(i); snrlin = db2pow(snrdb); % Received signal with noise through the channels yrx1 = h1 .* s(1,:) + h2 .* s(2,:) + sqrt(1/snrlin) * noise(1,:); yrx2 = h1 .* s(3,:) + h2 .* s(4,:) + sqrt(1/snrlin) * noise(2,:); % Alamouti STBC Decoding y1 = yrx1; y2 = yrx2; % Combined received signal r1 = conj(h1) .* y1 + h2 .* conj(y2); r2 = conj(h2) .* y1 - h1 .* conj(y2); % Normalize received signals h_combined = abs(h1).^2 + abs(h2).^2; r1 = r1 ./ h_combined; r2 = r2 ./ h_combined; % Combine received symbols r_combined = reshape([r1; r2], 1, []); % QPSK Demodulation ydemod = zeros(1, 2*N); ydemod(1:4:end) = real(r1) < 0; % In-phase component ydemod(2:4:end) = imag(r1) < 0; % Quadrature component ydemod(3:4:end) = real(r2) < 0; % In-phase component ydemod(4:4:end) = imag(r2) < 0; % Quadrature component % BER Calculation BERcount = sum(xor(ydemod, x)); ber(i) = BERcount / (2 * N); end % Plot BER vs SNR figure; semilogy(snr_db, ber, '-o'); grid on; xlabel('SNR (dB)'); ylabel('BER'); title('BER vs SNR for MISO QPSK with Alamouti STBC');
完
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