User Subgrouping in Multicast Massive MIMO over Spatially Correlated Rayleigh Fading Channels

TL;DR

This paper introduces a subgrouping strategy based on spatial channel characteristics for multicast massive MIMO systems, enhancing spectral efficiency and channel estimation, with optimal configurations and hybrid access methods.

Contribution

It proposes a novel user subgrouping approach based on spatial channels and analyzes optimal configurations and hybrid access strategies for multicast massive MIMO.

Findings

Subgrouping improves spectral efficiency and channel estimation.

Serving users in orthogonal time/frequency intervals outperforms pure spatial multiplexing.

Optimal subgroup configurations depend on antenna count and user distribution.

Abstract

Massive multiple-input-multiple-output (MaMIMO) multicasting has received significant attention over the last years. MaMIMO is a key enabler of 5G systems to achieve the extremely demanding data rates of upcoming services. Multicast in the physical layer is an efficient way of serving multiple users, simultaneously demanding the same service and sharing radio resources. This work proposes a subgrouping strategy of multicast users based on their spatial channel characteristics to improve the channel estimation and precoding processes. We employ max-min fairness (MMF) power allocation strategy to maximize the minimum spectral efficiency (SE) of the multicast service. Additionally, we explore the combination of spatial multiplexing with orthogonal (time/frequency) multiple access. By varying the number of antennas at the base station (BS) and users' spatial distribution, we also provide the optimal subgroup configuration that maximizes the spectral efficiency per subgroup. Finally, we show that serving the multicast users into two orthogonal time/frequency intervals offers better performance than only relying on spatial multiplexing.