在LMS自适应均衡器算法的基础上，将其中的LMS的迭代过程换成了RLS算法，如果需要研究RLS自适应均衡器性能的影响参数的话，可以尝试调整均衡器系数和遗忘因子的大小。程序代码如下
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%% RLS算法 实数基带系统仿真程序
% 功能：实数基带系统RLS均衡算法仿真-->BER\MSE指标
% 编写：李振兴
% E-Mail:lizhenxing@126.com
% Tel:
% 任何问题以及错误欢迎通过邮箱交流
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clc
clear all
close all hidden
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%% 初始化参数设置（用户可根据需要修改此处参数）
nFrame = 1;                          % 仿真发送的帧数（可作为蒙特卡洛仿真次数）
nPacket = 5000;                      % 每帧的发送的数据点数
step = 0.01;                        % 学习步长
dB = 25;                             % 信噪比
e = zeros(1,nPacket);                % 瞬时计算误差
E = zeros(1,nPacket);                % 统计均方误差（为平滑用）
h = [0.18,0.3,1,0.18];               %ISI信道参数
h = h/norm(h);
nW = 25;                        % 均衡器阶数
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% 注意：当帧数nFrame不等于1的时候不要绘制星座图！
% nFrame根据需要可以适当设置，只要使得MSE曲线足够平滑就可以
% 均衡器阶数对结果影响较大，这里给出的是经过实验的最佳阶数
% 学习步长不宜设置过大
%% 算法实现过程
LP = nPacket-nW;                 % 移掉几个数据避免负或零下标
X = zeros(nW+1,LP);              % 组织均衡器输入矩阵

% 循环开始（迭代次数=nFrame*nPacket）
for kk = 1:nFrame       % 此处修改蒙特卡洛次数
    W = zeros(nW+1,1);
    sX = round(rand(1,nPacket))*2-1; % BPSK 或 2PAM 信号
%     scatterplot(sX);               % nFrame=1时调试用
    rX = filter(h,1,sX);             % 通过信道
%     scatterplot(rX);               % nFrame=1时调试用
    vn = randn(1,nPacket);           % 产生噪声数据
    vn = vn/norm(vn)*10^(-dB/20)*norm(rX);  % 根据信噪比调整噪声功率
    SNR = 20*log10(norm(rX)/norm(vn));      % 计算SNR
    rX = rX+vn;                             % 接收信号
    XX = sign(rX);
   [num,rate] = symerr(XX,sX)               % 计算均衡前的误码
%     scatterplot(rX);                      % nFrame=1时调试用
    for i=1:LP
         X(:,i)=rX(i+nW:-1:i).';
    end
    %% RLS 迭代算法%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %初始化RLS相关参数%
    lambda = 1.2;        %遗忘因子
    beta = std(rX)^2;     %信号功率   
    P = 1/beta*eye(nW+1,nW+1);  %初始化相关矩阵
    % 开始均衡器参数调整
    for i = 1:LP
        K = (lambda^(-1)*P*X(:,i))/(1+lambda^(-1)*(X(:,i))'*P*X(:,i)); %计算增益矢量
        e(i) = W'*X(:,i)-sX(i);                    %误差
        W = W - conj(K)*e(i);                      %权值更新
        P = lambda^(-1)*P-lambda^(-1)*K*(X(:,i))'*P; %相关矩阵更新
        E(i) = E(i)+e(i)^2;                          %均方误差值
    end
  
    % 计算误码
    rEx = W'*X;                         % 均衡后信号
%   scatterplot(rEx)                  % nFrame=1时调试用
    sDx = sign(rEx);                  % 判决信号
    sErX = sDx-sX(1:length(sDx));         % 寻找误码
    BER=length(find(sErX~=0))/length(sErX) 
 end


 % 平滑误差 
 plot(10*log10(E))