Multicast Multigroup Beamforming for Per-antenna Power Constrained Large-scale Arrays

TL;DR

This paper introduces a low-complexity iterative method for multicast multigroup beamforming in large-scale antenna arrays with per-antenna power constraints, improving efficiency and accuracy over traditional relaxation techniques.

Contribution

It extends feasible point pursuit and successive convex approximation to handle practical per-antenna power constraints, enabling efficient large-scale array beamforming.

Findings

Method approaches relaxed upper bounds effectively.

Significant complexity reduction in large-scale systems.

Improved performance over semi-definite relaxation in non rank-1 solutions.

Abstract

Large in the number of transmit elements, multi-antenna arrays with per-element limitations are in the focus of the present work. In this context, physical layer multigroup multicasting under per-antenna power constrains, is investigated herein. To address this complex optimization problem low-complexity alternatives to semi-definite relaxation are proposed. The goal is to optimize the per-antenna power constrained transmitter in a maximum fairness sense, which is formulated as a non-convex quadratically constrained quadratic problem. Therefore, the recently developed tool of feasible point pursuit and successive convex approximation is extended to account for practical per-antenna power constraints. Interestingly, the novel iterative method exhibits not only superior performance in terms of approaching the relaxed upper bound but also a significant complexity reduction, as the dimensions of the optimization variables increase. Consequently, multicast multigroup beamforming for large-scale array transmitters with per-antenna dedicated amplifiers is rendered computationally efficient and accurate. A preliminary performance evaluation in large-scale systems for which the semi-definite relaxation constantly yields non rank-1 solutions is presented.