Statistical CSI Based Hybrid mmWave MIMO-NOMA with Max-Min Fairness

TL;DR

This paper proposes a statistical CSI-based hybrid beamforming method for mmWave NOMA systems that enhances user fairness under max-min constraints, addressing the challenge of limited CSI accuracy.

Contribution

It introduces a novel hybrid beamforming design using statistical CSI and user grouping, with digital beamforming based on SLNR, improving fairness in mmWave NOMA systems.

Findings

Outperforms previous algorithms in user fairness.

Effective beamforming with limited CSI accuracy.

Demonstrates the benefits of SCSI-based design in mmWave NOMA.

Abstract

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider mmWave NOMA systems with max-min fairness constraints. On the one hand, existing beamforming designs aiming at maximizing the spectrum efficiency (SE) are unsuitable for the NOMA systems with fairness in this paper. On the other hand, previous work on about mmWave NOMA mostly depends on full knowledge of channel state information (CSI) which is extremely difficult to obtain accurately in mmWave communication systems. To address this problem, we propose a heuristic hybrid beamforming design based on the statistical CSI (SCSI) user grouping strategy. An analog beamforming scheme is first proposed to integrate the whole cluster users to mitigate the inter-cluster interference in the first stage. Then two digital beamforming designs are proposed to further suppress the interference based on SCSI. One is the widely used zero forcing (ZF) approach and the other is derived from the signal-to leakage-plus-noise ratio (SLNR) metric extended from orthogonal multiple access (OMA) systems. The effective gains fed back from the users are used for the power allocation. We introduce the quadratic transform (QT) method and bisection approach to reformulate this complex problem so as to rend it solvable. Simulation results show that our proposed algorithms outperform the previous algorithms in term of user fairness.