基于生成对抗网络的MIMO信道估计方法

GAN-based channel estimation for massive MIMO system

作为5G的一项关键技术,大规模多输入多输出(MIMO)系统通过在基站配备大量天线可以显著提高频谱效率和能效。然而,在大规模MIMO系统中,精确的信道估计面临严峻挑战。为了在导频序列长度小于发射天线数量以及信道噪声强烈的情况下进行精确的估计信道,提出了一种基于生成对抗网络的大规模MIMO信道估计方法N2N-GAN。N2N-GAN首先对接收端的导频信号进行去噪,然后使用条件生成对抗网络根据去噪后的导频信号估计信道矩阵。仿真实验证明,与传统的信道估计算法和基于深度学习的算法相比,N2N-GAN对环境噪声具有更高的鲁棒性,而且可以适应更少导频符号和更多天线数量的场景。

As a key technology of 5G,massive MIMO system can significantly improve spectrum efficiency and energy efficiency by equipping a large number of antennas in base stations.However,in massive MIMO system,accurate channel estimation faces severe challenges.In order to estimate the channel accurately when the pilot sequence length is smaller than the number of transmitting antennas and the channel noise is strong,the estimation method N2N-GAN was proposed.N2N-GAN firstly denoised the pilot channel at the receiving end,and then used the conditional generative adversarial network to estimate the channel matrix according to the denoised pilot signal.Simulation experiments show that N2N-GAN achieves better robustness against noise compared with traditional channel estimation algorithms and deep learning-based methods.Meanwhile,it can adapt to scenarios with fewer pilot symbols and more antennas.