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基于MATLAB实现SLM、PTS和Clipping三种PAPR抑制算法

news 2026/5/12 17:45:35

基于MATLAB实现SLM、PTS和Clipping三种PAPR抑制算法，包含CCDF曲线绘制

一、核心代码

%% 参数设置
clear; clc; close all;
N = 128;          % 子载波数
QAM = 16;         % 调制方式
iter = 1000;      % 仿真次数
SNR = 20;         % 信噪比(dB)
clip_threshold = 6; % 限幅阈值(dB)%% 生成测试信号
data = randi([0 QAM-1], N, 1);
tx = qammod(data, QAM, 'UnitAveragePower', true);%% 1. Clipping方法
tic;
clipped = clip_signal(tx, clip_threshold);
[ccdf_clip, papr_clip] = calc_ccdf(clipped, 100);
time_clip = toc;%% 2. SLM方法
tic;
[tx_slm, ccdf_slm, papr_slm] = slm_processing(tx, iter);
time_slm = toc;%% 3. PTS方法
tic;
[tx_pts, ccdf_pts, papr_pts] = pts_processing(tx, 4, iter);
time_pts = toc;%% 结果可视化
figure;
subplot(2,1,1);
plot(papr_clip, ccdf_clip, 'r-o', papr_slm, ccdf_slm, 'b-s', papr_pts, ccdf_pts, 'g-d');
xlabel('PAPR (dB)'); ylabel('CCDF');
legend('Clipping', 'SLM', 'PTS');
title('PAPR抑制效果对比');subplot(2,1,2);
bar([time_clip, time_slm, time_pts]);
ylabel('计算时间(s)');
xticklabels({'Clipping', 'SLM', 'PTS'});
title('算法复杂度对比');

二、关键算法

1. 限幅(Clipping)算法

function [tx_clip, ccdf, papr] = clip_signal(tx, threshold)% 限幅处理tx_clip = tx;peak = max(abs(tx_clip));tx_clip(abs(tx_clip) > threshold*peak) = threshold*peak;% CCDF计算[papr, ccdf] = calc_ccdf(tx_clip, 100);
endfunction [papr, ccdf] = calc_ccdf(signal, num_bins)[values, ~] = hist(abs(signal).^2, num_bins);cdf = cumsum(values)/sum(values);papr = 10*log10(values);ccdf = 1 - cdf;
end

2. 选择性映射(SLM)算法

function [tx_slm, ccdf, papr] = slm_processing(tx, iter)N = length(tx);phase_sets = exp(1j*2*pi*(0:3)/4); % 4种相位序列papr_values = zeros(1, iter);best_sequence = tx;for i = 1:iter% 生成候选信号phase_idx = randi(4);tx_candidate = tx .* phase_sets(phase_idx);% IFFT变换tx_ifft = ifft(tx_candidate);% 计算PAPRpeak = max(abs(tx_ifft));avg = mean(abs(tx_ifft));papr_values(i) = 10*log10(peak/avg);% 更新最优序列if papr_values(i) < 10*log10(max(abs(tx))/mean(abs(tx)))best_sequence = tx_candidate;endend% 选择最优序列tx_slm = ifft(best_sequence);[papr, ccdf] = calc_ccdf(tx_slm, 100);
end

3. 部分传输序列(PTS)算法

function [tx_pts, ccdf, papr] = pts_processing(tx, V, iter)N = length(tx);block_size = floor(N/V);papr_values = zeros(1, iter);best_phase = ones(1,V);for i = 1:iter% 分割子块phases = exp(1j*2*pi*(0:1)/2); % 2种相位因子for v = 1:Vblock = tx((v-1)*block_size+1:v*block_size);phase_idx = randi(2);block = block .* phases(phase_idx);tx((v-1)*block_size+1:v*block_size) = block;end% IFFT变换tx_ifft = ifft(tx);% 计算PAPRpeak = max(abs(tx_ifft));avg = mean(abs(tx_ifft));papr_values(i) = 10*log10(peak/avg);% 更新最优相位if papr_values(i) < 10*log10(max(abs(tx))/mean(abs(tx)))best_phase = phases(randi(2),:);endend% 应用最优相位tx_pts = tx;for v = 1:Vblock = tx_pts((v-1)*block_size+1:v*block_size);block = block .* best_phase(v);tx_pts((v-1)*block_size+1:v*block_size) = block;end[papr, ccdf] = calc_ccdf(tx_pts, 100);
end