Gaussian mixture-learned approximate message passing (GM-LAMP) based hybrid precoders for mmWave massive MIMO systems

Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. In this paper, Gaussian Mixture learned approximate message passing (GM-LAMP) network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems. Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit (OMP) and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high. This drawback can be addressed using classical iterative algorithms such as approximate message passing (AMP), which has comparatively low computational complexity. The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders. In this paper, the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP (LAMP) network, and is further enhanced as GM-LAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders. The simulation results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates, better accuracy and low computational complexity compared to the existing algorithms.