基于改进SRGAN的OFDM信道估计方法

在正交频分复用(OFDM)系统的信道估计过程中，传统信道插值算法是建立在假设导频附近处的估计值存在关联的基础上，当信道特征因无线信道的时变、频变特性而不连续时，估计结果将不理想。针对这一问题，本文引入超分辨率重建模型SRGAN的改进模型——SRWGAN，替代信道估计中的插值处理。在SRWGAN模型中，将导频处的最小二乘(LS)估计值类比于低分辨率图像中的像素点，先通过卷积网络提取信道特征，再通过多个残差网络学习映射关系，然后经上采样层放大，最后通过判别网络WGAN不断判别并提升估计效果。实验结果表明，基于SRWGAN的信道估计效果优于传统的信道估计算法，且与同类型的SRCNN模型相比，同等条件下，当误码率相同时，信噪比(SNR)提升约3 dB，当MSE值相同时，SNR提升约5 dB。

OFDM Channel Estimation Based on Improved SRGAN

In the channel estimation process of the orthogonal frequency division multiplexing (OFDM) system, the traditional channel interpolation algorithm is based on the assumption that the estimated values near the pilot are correlated. When the channel characteristics are discontinuous due to the time-varying and frequency-varying characteristics of the wireless channel, the estimation results will be unsatisfactory. In response to this problem, this paper introduces an improved model SRWGAN of super-resolution reconstruction model SRGAN to replace the interpolation processing in channel estimation. In the model SRWGAN, the least squares (LS) estimation value at the pilot is analogous to the pixels in the low-resolution image. The channel features are first extracted through the convolutional network, and then the mapping relationship is learned through multiple residual networks. Then it is amplified by the up-sampling layer, and finally the discriminant network WGAN is used to continuously discriminate and improve the estimation effect. The experimental results show that the channel estimation effect based on SRWGAN is better than the traditional channel estimation algorithm, and compared with the same type of SRCNN model, under the same conditions, when the bit error rate is the same, the signal-to-noise ratio (SNR) is improved by about 3 dB, and when the MSE value is the same, the SNR is increased by about 5 dB.