【通信】对集成中继+可重构智能表面(RIS)辅助无人机通信系统采用选择合并(SC)技术的性能分析模拟附matlab代码
✅作者简介：热爱科研的Matlab仿真开发者，擅长毕业设计辅导、数学建模、数据处理、程序设计科研仿真。
🍎完整代码获取 定制创新 论文复现点击：Matlab科研工作室
👇 关注我领取海量matlab电子书和数学建模资料
🍊个人信条：做科研，博学之、审问之、慎思之、明辨之、笃行之，是为：博学慎思，明辨笃行。
🔥 内容介绍
一、引言
随着无人机技术的广泛应用，无人机通信面临着诸多挑战，如信号遮挡、路径损耗等。集成中继与可重构智能表面（RIS）技术为改善无人机通信性能提供了有效途径。选择合并（SC）作为一种简单且有效的分集合并技术，可进一步提升系统性能。通过对这一集成系统采用 SC 技术进行性能分析模拟，有助于深入理解其工作机制和性能表现，为实际应用提供理论支持。
二、系统模型
集成中继 + RIS 辅助无人机通信系统架构
- 无人机（UAV）
：作为通信终端，在空间中移动执行任务，与地面基站（BS）进行通信。无人机配备有通信天线，用于接收和发送信号。
- 中继节点（RN）
：部署在合适位置，负责接收无人机发送的信号，并对其进行放大或解码转发，以克服信号传播过程中的损耗。中继节点与无人机和基站之间通过无线链路进行通信。
- 可重构智能表面（RIS）
：由大量可独立调控的反射元件组成，可对入射信号进行相位、幅度等特性的调整。通过智能地改变反射信号的特性，RIS 能够增强无人机与基站之间的通信链路，改善信号传输质量。RIS 与无人机和基站之间存在反射链路。
信号传播模型
⛳️ 运行结果
📣 部分代码
%% Clear and close all
clear;
close all;
clc;
%% Parameters for Relay System
M = 1e6; % Number of samples (for simulation)
Average_SNR_dB = -30:5:50; % Average SNR in dB
Average_SNR = 10.^(Average_SNR_dB ./ 10); % Convert SNR to linear scale
Threshold_SNR_dB = -30; % Threshold SNR in dB
Gamma_0 = 10^(Threshold_SNR_dB / 10); % Convert threshold SNR to linear scale
% Nakagami-m parameters
m = 2; % Shape parameter
Omega_SR = 1; % Mean SNR of Source-Relay link
Omega_RD = 1; % Mean SNR of Relay-Destination link
% Gains
G_s = 10; G_r = 10; G_d = 10;
% Path Losses
d1 = 10; % Distance from source to relay in meters
d2 = 10; % Distance from relay to destination in meters
f = 1.6e9; % Frequency in Hz (1.6 GHz)
c = 3e8; % Speed of light in m/s
lambda = c / f; % Wavelength
% Free Space Path Loss parameters
PL1 = (G_s * G_r * lambda^2) / (4 * pi * d1)^2; % Source to Relay path loss
PL2 = (G_r * G_d * lambda^2) / (4 * pi * d2)^2; % Relay to Destination path loss
% Initialize arrays for outage probability simulation and analytical results
OutageProb_sim = zeros(size(Average_SNR_dB ));
OutageProb_analytical = zeros(size(Average_SNR_dB ));
%% Simulation loop over different transmit powers
for jj = 1:length(Average_SNR_dB )
% Generate Nakagami-m fading coefficients
h1 = sqrt(gamrnd(m, 1/m, 1, M)); % Nakagami-m fading coefficients for h1
h2 = sqrt(gamrnd(m, 1/m, 1, M)); % Nakagami-m fading coefficients for h2
% Apply path losses including FSPL
h1f = h1;
h2f = h2;
% Effective SNR considering path losses and transmit power
SNR1 = Average_SNR(jj) * h1f.^2 *(sqrt(PL1));
SNR2 = Average_SNR(jj) * h2f.^2*(sqrt(PL2)) ;
SNR = min(SNR1, SNR2); % Minimum SNR for outage calculation
% Calculate outage probability from simulation
OutageProb_sim(jj) = mean(SNR < Gamma_0);
end
%% Analytical outage probability calculation loop
for jj = 1:length(Average_SNR_dB )
% Compute CDF for Source-Relay link
% Analytical Outage Probability Calculation
gamma_th_sr = Gamma_0 /Omega_SR; % Normalized threshold for Nakagami-m CDF
F_gamma_SR = 1-gammainc(m *(1/sqrt(PL1))* gamma_th_sr./Average_SNR, m, 'lower');
gamma_th_rd = Gamma_0 /Omega_RD; % Normalized threshold for Nakagami-m CDF
F_gamma_RD = 1-gammainc(m *(1/sqrt(PL2))* gamma_th_rd./Average_SNR, m, 'lower');
% Compute outage probability analytically
OutageProb_analytical = 1-(F_gamma_SR.*F_gamma_RD);
end
%% Plotting Outage Probability results
figure;
semilogy(Average_SNR_dB , OutageProb_sim, 'r-s', 'LineWidth', 1.5);
hold on;
semilogy(Average_SNR_dB , OutageProb_analytical, 'k--', 'LineWidth', 1.5);
xlabel('Transmit Power (dB)');
ylabel('Outage Probability');
title('Outage Probability vs Transmit Power for Nakagami-m Fading Channel with Relay System (Including FSPL)');
legend('Simulation', 'Analytical');
grid on;
axis([-30 10 1e-6 1])