Rotatable Antenna Enhanced Multicast Communication System

TL;DR

This paper proposes an RA-enhanced multicast system that optimizes beamforming and antenna orientation to improve fairness among users, using an iterative AO algorithm with convex approximations.

Contribution

It introduces a joint optimization framework for beamforming and antenna orientation in multicast systems, enhancing fairness performance.

Findings

RA-based scheme significantly improves fairness over fixed and random orientations.

The proposed AO algorithm effectively solves the non-convex max-min SINR problem.

Simulation results confirm the advantage of RA in multicast communication.

Abstract

Rotatable antenna (RA) provides additional spatial degrees of freedom (DoFs) for communication systems by enabling per-antenna dynamic boresight adjustment, which is attractive for fairness-oriented multicast transmission. This letter investigates an RA-enhanced downlink multi-group multicast system. Specifically, we aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all users by jointly optimizing the multicast beamforming vectors and the RA boresight directions under transmit power and rotation constraints. To solve this non-convex problem, we first reformulate the max-min SINR objective via quadratic transform. Then, we develop an alternating optimization (AO) algorithm that iteratively updates the multicast beamforming and RA boresight directions. The beamforming vectors are obtained from a convex subproblem, while the boresight directions are refined using a successive convex approximation (SCA) procedure. Simulation results verify that the proposed RA-based scheme substantially enhances the fairness performance compared with fixed antenna-based and random-orientation benchmarks.