A Deep Learning Algorithm for Beamspace Channel Estimation
In a Time Division Duplex(TDD)millimeter-wave massive Multiple-Input Multiple-Output(MIMO)system,because of the sparsity of the beamspace channel,the original high-dimensional channel is effectively reconstructed from low-dimensional measurement data.For the uplink,without considering sparsity,this study combines the traditional optimization algorithm with a data-driven deep learning method and proposes an improved beam spatial channel estimation algorithm based on deep learning.Starting from the reconstruction process,the network is constructed by alternately establishing a Gradient Descent Module(GDM)and a Proximal Mapping Module(PMM).Specifically,a theoretical formula is deduced according to the Saleh-Valenzuela channel model,and channel data are generated.Second,the data are transferred to a network comprising a fixed number of layers using the update step of the traditional Iterative Shrinkage Thresholding Algorithm(ISTA),and each layer corresponds to an iteration similar to that of ISTA.Finally,the trained model is tested online to restore the channel to be estimated.Through the construction of the PyTorch environment,the proposed algorithm is compared with the Orthogonal Matching Pursuit(OMP),Approximate Message Passing(AMP),Learnable AMP(LAMP),and Gaussian Mixture LAMP(GM-LAMP)algorithms.The results demonstrate that the proposed algorithm improves the estimation accuracy by approximately 3.07 and 2.61 dB compared with better deep learning algorithms,LAMP and GM-LAMP,and by approximately 11.12 and 9.57 dB with the traditional OMP and AMP algorithms.The number of parameters is approximately 39％and 69％less than those of LAMP and GM-LAMP algorithms,respectively.
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