基于生成对抗网络的MIMO信道估计方法

doi:10.11959/j.issn.1000-0801.2021135

Abstract

Abstract:

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.

Key words: channel estimation, massive MIMO, signaldenoising, generative adversarial network

CLC Number:

TP393

Yuxiu HUA, Rongpeng LI, Zhifeng ZHAO, Jianjun WU, Honggang ZHANG. GAN-based channel estimation for massive MIMO system[J]. Telecommunications Science, 2021, 37(6): 14-22.

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参数	值
工作频率	2.5 GHz
天线间隔	半波长
带宽	10 MHz
多径数量/个	10
天线数量/根	64、128、192、256
用户数量/个	32
导频序列长度	4、8、16

GAN-based channel estimation for massive MIMO system

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