基于集成神经网络的信道估计方法研究

为解决噪声以及信号干扰等因素严重影响接收端信道估计的问题，提出一种基于集成神经网络的信道估计方法，利用多个改进后的神经网络来提取带噪导频信号与原始导频信号之间的非线性关系模型，根据其输出误差来计算差异度值，并结合差异度对其进行集成，得到训练完成的集成神经网络模型，通过集成神经网络模型来获取信道估计结果；与最小二乘(least squares，LS)估计方法相比，该方法不仅可以提高通信的可靠性，而且还能减少导频开销提高通信有效性；在不同的调制方式下，当误码率相同时，算法所需的信噪比LS算法均要更低；基于正交频分复用(orthogonal frequency division multiple，OFDM)通信系统进行仿真，结果证明，该方法具有优良的性能。

Research on channel estimation method based on integrated neural network

In order to solve the problem that noise and signal interference have an effect on the channel estimation of receiver, the channel estimation method based on integrated neural network has been proposed in this thesis. Firstly, improved neural network will be used to extract nonlinear relationship between pilot signal with noise and original pilot signal. Then the diversity factor will be calculated based on its output errors, and the integrated neural network model will be obtained according to diversity factor. Finally, channel estimation results can be obtained by model. Compared with the LS estimation, not only can this method improve the reliability of the communication, but also reduce the cost of pilot to improve the effectiveness of communication. Under different modulation methods, when the bit error rate is the same, the signal-to-noise ratio (SNR) of LS algorithm is lower; based on orthogonal frequency division multiple (OFDM), the signal-to-noise ratio (SNR) of LS algorithm is lower. The simulation results of OFDM communication system show that the method has good performance.