为自适应生成QPSK符号过滤

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约翰pirsch 2018年3月1日

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这个问题直接联系

//www.tianjin-qmedu.com/matlabcentral/answers/385801-generating-qpsk-symbols-for-adaptive-filtering

评论道: 阿布·库马尔2020年12月11日

你好,一个任务我应该调查数据传输的自适应均衡器的性能。我首先应该先设计一个发生器模块用于创建一个复杂的信息符号序列,我们称为s [n]。这个任务我们将假定QPSK符号或换句话说,s [n]必须来自一组{+农协,a-ja——+农协,-a-ja},代表了信号幅度。谁能帮我在设计这个过滤器im很困谢谢

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约翰pirsch 2018年3月3日

这个项目的目的是研究一种自适应均衡器的性能对多路径通道datatransmission导致“传输干扰”(ISI)。系统模拟的basicconfiguration下一页上的图所示。请注意,我们不包括载波调制和解调,然而所有处理涉及complexarithmetic操作(复杂的符号作为输入,复杂的信道脉冲响应,等等)。有五个基本模块,必须纳入模拟:1。数据信号发生器模块用于创建一个复杂的信息符号序列[n]。这个实验我们将假定QPSK符号,或者换句话说这[n]必须bedrawn从集合{+农协、−ja−+农协,−−ja}, wherearepresents信号amplitudethat选择根据给定信噪比(信噪比)。假设噪声hasunit力量,那么信噪比= 20 log10 (√2 a)。2。通道滤波器模块是一个冲动的冷杉过滤器responsec [n]模拟thechannel distortion.3。噪声发生器模块用于生成添加剂噪声存在于任何数字通信系统。承担单位功率,噪声复杂的高斯噪声。 InMatlab, this can be done using the command:(1/sqrt(2))*(randn(1,N)+j*randn(1,N)),which generates a vector ofNnoise samples.4. The adaptive equalizer module is a lengthM+1 FIR filterh[n] whose coefficients are adjustedusing either the LMS or the normalized-LMS algorithm. (Remember: use the complex versionof the filter!)5. The decision device module takes the output of the equalizer and quantizes it to one of thefour possible transmitted symbols, based on whichever is closest. This module is necessarywhen your equalizer is operating in decision-directed mode, where the detected symbols areused to train the filter.The effectiveness of the equalizer in suppressing the ISI introduced by the channel may be seenby plotting the input and output of the equalizer in a two-dimensional (real-imaginary) display.If you run the full experiment a total ofPtimes, then you will havePdifferent values of theequalizer input and output for each time samplen, wheren= 0,···,N−1. If you plot thesePpoints for eachn, hopefully you will see that the output of the equalizer eventually converges tofour tightly clustered clouds around the QPSK symbol values. You should also plot the errore[n]as a function ofn, averaged over thePexperiments. This will give you a picture of the rate atwhich the equalizer is converging.Your main Matlab simulation should have the following as input variables: a vectorccontainingthe channel impulse response, the SNR in dB, the step sizeμfor the fixed-step LMS equalizer, thelengthMof the equalizer filterh, the number of training samplesTused to initially train the filter,the number of total samplesNper experiment, and the total numberPof experiments used toplot the results and compute the average error.To test the functionality of your code, use the following test case:c= [1 0.2−0.4 0.3 0.2−0.1]T,SNR = 25dB,μ= 0.01,M= 10,N= 300,T= 30,P= 100. You should get results similar to those shown in class. For your report, investigate the effect of different channels (including complexchannels), different SNR values, step sizes, and filter lengths. Comment on the relative performanceof the LMS and normalized-LMS algorithms, touching on convergence rate, stability and the finalvalue of the error. Recall that channel filters whose first tap has the largest value will likely leadto the best performance. Channels whose strongest tap is not the first require the addition of thedelay (shown in the figure below) to ensure that the error signal is being computed by comparingthe transmitted symbol from the output of the filter that should correspond to that transmittedsymbol. To avoid the need for the delay block, choose channel impulse responses whose first tap islarger than the rest.

阿布·库马尔 2020年12月11日

看看这个链接

https://www.andrewtec.me/post/adaptive-eq/

登录置评。

在回答这个问题。

答案(2)

亚伯拉罕Boayue 2018年3月2日

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直接链接到这个答案

//www.tianjin-qmedu.com/matlabcentral/answers/385801-generating-qpsk-symbols-for-adaptive-filtering answer_308066

                             函数[e, w, y, w] = LMS (x, d,μM)
                            
                             % lms algotherm
                            
                             N =长度(x);
                            
                             w = 0 (M, 1);
                            
                             e = 0 (1, N);
                            
                             y = 0 (1, N);
                            
                             u = 0(1米);
                            
                             W = 0 (M, N);
                            
                             为k = 1: N
                            
                             u = [x (k), u (1: m - 1));%向量帐目的输入信号
                            
                             %权值非零
                            
                             y (k) = u * w;
                            
                             e (k) = d (k) - y (k);
                            
                             w = w +μ* u ' * e (k);
                            
                             W (:, k) = W;
                            
                             结束
                            
                             结束
                            
                             % % m文件
                            
                             关闭所有
                            
                             清晰的
                            
                             n = 1:3;
                            
                             D = (2.9 - 3.1 3.3 - 3.5);
                            
                             % h = 0.5 * (1 + cos(2 *π* (n - 2) / D (4)));
                            
                             %茎(n、h、“填满”);网格
                            
                             % xlim ([0 5]);
                            
                             M = 11;
                            
                             N = 1000;
                            
                             L = 800;
                            
                             J_ave = 0 (1, N);
                            
                             延迟= 7;
                            
                             μ= 0.075;
                            
                             J_vec = 0(长度(D), N);%向量存储值的均方误差
                            
                             V_w = 0(米、长度(D));%向量存储估计系数
                            
                             为k = 1:长度(D)
                            
                             h = 0.5 * (1 + cos(2 *π* (n - 2) / D (k)));%通道响应
                            
                             xn =符号(randn (1, N) -0.5);%这是伯努利序列;生成
                            
                             %这里正交相移编码
                            
                             为i = 1: L
                            
                             % d =[0(延迟)xn (1,1: N-delay)];
                            
                             d = 0 (1, N);
                            
                             d(延迟:N) = xn (1: N-delay + 1);
                            
                             x1 =过滤器(h 1 xn);
                            
                             v = sqrt (0.001) * randn (1, N);
                            
                             x = x1 + v (1: N);
                            
                             [e, w, y, w] = LMS (x, d,μM);%执行均衡
                            
                             J_ave = J_ave + abs (e。^ 2);
                            
                             结束
                            
                             :J_vec (k) = J_ave;
                            
                             V_w (:, k) = w;
                            
                             结束
                            
                             J_vec = J_vec / L;
                            
                             % J_vec = 10 * log10 (J_vec);
                            
                             印第安纳州= 0:n - 1;
                            
                             % %平滑曲线
                            
                             窗口= 1;
                            
                             为我= 1:N-window
                            
                             J_vec (i) =意味着(J_vec(我+窗口));
                            
                             结束
                            
                             图
                            
                             :semilogy(印第安纳州,J_vec (1),“线宽”,1.5,“颜色”,“k”);在
                            
                             semilogy(印第安纳州,J_vec (2:)“线宽”,1.5,“颜色”,“米”)
                            
                             semilogy(印第安纳州,J_vec (3:)“线宽”,1.5,“颜色”,“r”)
                            
                             semilogy(印第安纳州,J_vec (4:)“线宽”,1.5,“颜色”,“b”)
                            
                             网格
                            
                             一个=标题(“Ensamble-average | e (i) | ^ 2》);
                            
                             集(,“字形大小”14);
                            
                             一个=包含(“不。迭代的);
                            
                             集(,“字形大小”14);
                            
                             一个= ylabel (“MSE dB”);
                            
                             集(,“字形大小”14);
                            
                             ylim ([10 ^ 3 ^ 1])
                            
                             xlim (500 [0])
                            
                             图
                            
                             茎(V_w (: 1),“填充”,“米”);网格