基于深度学习的智能反射面辅助信道估计OA

Aiming at the problem that the intelligent reflecting surface(IRS)lacks a radio frequency(RF)chain and it is dif-ficult to obtain more accurate channel state information(CSI),this paper proposes a channel estimation method based on deep re-sidual learning.For an IRS-assisted orthogonal frequency division multiplexing(OFDM)system,in which a convolutional network(CNN)module with element-wise subtraction is set up to remove noise in the channel,IRS is considered a promising technique,which is achieved by adjusting wireless environment to improve spectrum and energy utilization,the industry regards it as a poten-tial 6G wireless communication technology,channel estimation is one of the main tasks of using IRS-assisted communication,CSI is to design the optimal wireless communication system in the IRS-assisted communication system.Source beamforming is a key fac-tor,so an accurate channel estimation algorithm is crucial for IRS-assisted communication.Simulation results show that the deep learning method proposed in this paper has better performance than traditional methods.