User Subgrouping in Scalable Cell-Free Massive MIMO Multicasting Systems

TL;DR

This paper proposes a subgroup-based multicast framework for cell-free massive MIMO systems, improving spectral efficiency by grouping users with similar channels and employing scalable precoding strategies, especially in clustered user scenarios.

Contribution

Introduces a novel user subgrouping approach in CF-mMIMO multicasting that enhances efficiency and scalability with two precoding strategies, tailored for different user distributions.

Findings

Unicast with IP-MMSE is optimal for uniformly distributed users.

Multicast subgrouping improves sum spectral efficiency in clustered scenarios.

Distributed CB precoding outperforms IP-MMSE for per-user spectral efficiency in clustered environments.

Abstract

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a breakthrough technology for beyond-5G systems, designed to significantly boost the energy and spectral efficiencies of future mobile networks while ensuring a consistent quality of service for all users. Additionally, multicasting has gained considerable attention recently because physical-layer multicasting offers an efficient method for simultaneously serving multiple users with identical service demands by sharing radio resources. Typically, multicast services are delivered either via unicast transmissions or a single multicast transmission. This work, however, introduces a novel subgroup-centric multicast CF-mMIMO framework that divides users into several multicast subgroups based on the similarities in their spatial channel characteristics. This approach allows for efficient sharing of the pilot sequences used for channel estimation and the precoding filters used for data transmission. The proposed framework employs two scalable precoding strategies: centralized improved partial MMSE (IP-MMSE) and distributed conjugate beam-forming (CB). Numerical results show that for scenarios where users are uniformly distributed across the service area, unicast transmissions using centralized IP-MMSE precoding are optimal. However, in cases where users are spatially clustered, multicast subgrouping significantly improves the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Notably, in clustered scenarios, distributed CB precoding outperforms IP-MMSE in terms of per-user SE, making it the best solution for delivering multicast content.