Capacity Optimal Generalized Multi-User MIMO: A Theoretical and Practical Framework

Abstract

Conventional multi-user multiple-input multipleoutput (MU-MIMO) mainly focused on Gaussian signaling, independent and identically distributed (IID) channels, and a limited number of users. It will be laborious to cope with the heterogeneous requirements in next-generation wireless communications, such as various transmission data, complicated communication scenarios, and unprecedented massive user access. Therefore, this paper studies a generalized MU-MIMO (GMU-MIMO) system with more practical constraints, i.e., non-Gaussian signaling, non-IID channel, and massive users and antennas. These generalized assumptions bring new challenges in theory and practice. For example, there is no accurate capacity analysis for GMU-MIMO. In addition, it is unclear how to achieve the capacity optimal performance with practical complexity.To address these challenges, a unified framework is proposed to derive the GMU-MIMO capacity and design a capacity optimal transceiver, which jointly considers encoding, modulation, detection, and decoding. Group asymmetry is developed to make a tradeoff between user rate allocation and implementation complexity. Specifically, the capacity region of group asymmetric GMU-MIMO is characterized by using the celebrated mutual information and minimum mean-square error (MMSE) lemma and the MMSE optimality of orthogonal approximate message passing (OAMP)/vector AMP (VAMP). Furthermore, a theoretically optimal multi-user OAMP/VAMP receiver and practical multi-user low-density parity-check (MU-LDPC) codes are proposed to achieve the capacity region of group asymmetric GMU-MIMO. Numerical results demonstrate that the gaps between theoretical detection thresholds of the proposed framework with optimized MU-LDPC codes and quadrature phase-shift keying (QPSK) modulation and the sum capacity of GMU-MIMO are about 0.2 dB. Moreover, their finite-length performances are about 1∼2 dB away from the associated sum capacity.

A Tutorial on Decoding Techniques of Sparse Code Multiple Access

“…In addition to the above points, the SCMA detectors should also be designed for emerging wireless technologies, such as beamforming, massive number of antennas in MIMO, multi-user MIMO, reconfigurable intelligent surface (RIS), etc [131].…”