Beamspace Channel Estimation Algorithm Based on Deep Compressed Sensing

In the millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system with lens antenna array, because the radio frequency (RF) link is much less than the number of antennas, it is necessary to recover the high-dimensional channel from the low-dimensional effective measurement signal by channel estimation. The current channel estimation methods basically make use of the sparsity of the beamspace channel, transforming the channel estimation into compressed sensing problem and then estimating with different methods. Aiming at the limitation that approximate message passing (AMP) algorithm needs channel prior information in channel estimation, this paper proposed an improved channel estimation algorithm. Firstly, a new noise term was derived based on the AMP algorithm and fitted with a convolutional neural network (CNN). Then the iterative denoising process was expanded into a deep network to solve the linear inverse transformation of the measurement signal to the channel. Finally, the initially estimated channel was further optimized by a residual noise removal network. In addition, the controllable parameters were introduced to increase the flexibility of the channel estimation process, and the sensing matrix was jointly trained with other network parameters to improve the channel estimation accuracy. This paper verified the proposed algorithm from two aspects of channel estimation accuracy and system transmission quality, and carried out the theoretical formula derivation and system simulation analysis on the Saleh-Valenzuela channel model. Simulation results show that the proposed algorithm has less model parameters and computation than the traditional algorithm, and can improve the accuracy of channel estimation and the transmission quality of the communication system.