Spectral Efficiency of Unicast and Multigroup Multicast Transmission in Cell-free Distributed Massive MIMO Systems

TL;DR

This paper analyzes spectral efficiency in cell-free massive MIMO systems supporting unicast and multicast, deriving closed-form rate expressions, proposing power allocation schemes, and demonstrating the benefits of joint transmission strategies.

Contribution

It introduces a joint unicast and multicast transmission framework with derived CSI estimation, closed-form rate expressions, and a novel deep learning-based power optimization approach.

Findings

Closed-form achievable rate expressions are validated.

Joint transmission improves spectral efficiency trade-offs.

Deep learning enhances computational efficiency of power allocation.

Abstract

In this paper, we consider a joint unicast and multi-group multicast cell-free distributed massive multiple-input multiple-output (MIMO) system, while accounting for co-pilot assignment strategy based channel estimation, pilot contamination and different precoding schemes. Under the co-pilot assignment strategy, we derive the minimum-mean-square error (MMSE) channel state information (CSI) estimation for unicast and multicast users. Given the acquired CSI, the closed-form expressions for downlink achievable rates with maximum ratio transmission (MRT), zero-forcing (ZF) and MMSE beamforming are derived. Based on these expressions, we propose an efficient power allocation scheme by solving a multi-objective optimization problem (MOOP) between maximizing the minimum spectral efficiency (SE) of multicast users and maximizing the average SE of unicast users with non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the MOOP is converted into a deep learning (DL) problem and solved by an unsupervised learning method to further promote computational efficiency. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the joint unicast and multigroup multicast transmission scheme in cell-free distributed massive MIMO systems. The SE analysis under various system parameters and the trade-off regions between these two conflicting optimization objectives offers numerous flexibilities for system optimization.