Joint AP Selection and Power Allocation for Unicast-Multicast Cell-Free Massive MIMO

TL;DR

This paper develops a joint optimization framework for AP selection and power allocation in cell-free massive MIMO systems supporting both unicast and multicast transmissions, improving spectral efficiency with practical constraints.

Contribution

It introduces a novel optimization approach with closed-form spectral efficiency expressions and efficient algorithms for joint AP selection and power control in unicast-multicast massive MIMO.

Findings

Joint optimization significantly improves spectral efficiency.

APG algorithm reduces complexity while maintaining performance.

Proposed methods outperform benchmarks in various scenarios.

Abstract

Joint unicast and multicast transmissions are becoming increasingly important in practical wireless systems, such as Internet of Things networks. This paper investigates a cell-free massive multiple-input multiple-output system that simultaneously supports both transmission types, with multicast serving multiple groups. Exact closed-form expressions for the achievable downlink spectral efficiency (SE) of both unicast and multicast users are derived for zero-forcing and maximum ratio precoding designs. Accordingly, a weighted sum SE (SSE) maximization problem is formulated to jointly optimize the access point (AP) selection and power allocation. The optimization framework accounts for practical constraints, including the maximum transmit power per AP, fronthaul capacity limitations between APs and the central processing unit, and quality-of-service requirements for all users. The resulting non-convex optimization problem is reformulated into a tractable structure, and an accelerated projected gradient (APG)-based algorithm is developed to efficiently obtain near-optimal solutions. As a performance benchmark, a successive convex approximation (SCA)-based algorithm is also implemented. Simulation results demonstrate that the proposed joint optimization approach significantly enhances the SSE across various system setups and precoding strategies. In particular, the APG-based algorithm achieves substantial complexity reduction while maintaining competitive performance, making it well-suited for large-scale practical deployments.