Quantized Hybrid Precoding for Massive Multiuser MIMO with Insertion Loss

Abstract

In this paper, we study the hybrid precoding structures for massive multiuser multiple-input multiple-output (MIMO) systems. Particularly, the practical hardware network models with insertion loss are developed. The achievable rates of two typical hybrid precoding structures, the fully-connected structure and the sub- connected structure, are investigated, from which we discover that the sub-connected structure always outperforms the fully-connected structure in terms of the achievable rate in massive MIMO. We further characterize the effect of quantized analog precoding which indicates that the subconnected structure is able to achieve better performance with fewer feedback bits than the fully-connected structure. We also propose a channel statistics-based codebook used for the digital precoding stage which is more suitable for hybrid precoding systems than the conventional random vector quantization (RVQ) codebook.