基于改进遗传算法的信道估计方案

为了提高通信系统信道估计的准确率,同时适应更大的数据量,进行更加复杂的数据计算,引入神经网络的方法进行信道估计,采用了BP和RBF神经网络进行实验对比,与传统信道估计方式相比有明显提升;在此基础上,进一步提出基于改进遗传算法优化的 RBF 神经信道估计方法,目的是帮助确定 RBF 网络的隐藏层参数, 使得网络的参数趋于全局最优解,信道估计器的性能从而得到提升。经过 MATLAB 仿真,改进后的RBF神经网络可以更好地解决信道估计问题,验证了此方法的可行性。

Channel Estimation Scheme Based on Improved Genetic Algorithm

In order to improve the accuracy of channel estimation in communication systems and adapt to larger data volumes for more complex data calculations, a neural network method was introduced for channel estimation. An RBF neural network and a BP neural network were harnessed for an experimental comparison, demonstrating a notable enhancement over conventional channel estimation techniques. Building on this, an RBF neural channel estimation method optimized by a genetic algorithm was also suggested. This is intended to assist in establishing the hidden layer parameters of the RBF network, steering the network parameters toward the universally optimal solution and thereby boosting the efficacy of the channel estimator.The enhanced RBF neural networks capability to effectively resolve the channel estimation issue is confirmed by the MATLAB simulation, thereby demonstrating the viability of this approach.