Coordinated Multicell Multicast Beamforming Based on Manifold Optimization

TL;DR

This paper introduces a novel manifold optimization approach for coordinated multicell multicast beamforming, enhancing performance and convergence in wireless networks.

Contribution

It reformulates the multicast beamforming problem as a parametric manifold optimization and proposes a low-complexity, guaranteed-convergence algorithm.

Findings

Outperforms existing SDP-based and DC-programming methods.

Achieves near-optimal performance in simulations.

Converges reliably with low computational complexity.

Abstract

Multicast beamforming is a key technology for next-generation wireless cellular networks to support high-rate content distribution services. In this paper, the coordinated downlink multicast beamforming design in multicell networks is considered. The goal is to maximize the minimum signal-to-interference-plus-noise ratio of all users under individual base station power constraints. We exploit the fractional form of the objective function and geometric properties of the con-straints to reformulate the problem as a parametric manifold optimization program. Afterwards we propose a low-complexity Dinkelbach-type algorithm combined with adaptive exponential smoothing and Riemannian conjugate gradient iteration, which is guaranteed to converge. Numerical experiments show that the proposed algorithm outperforms the existing SDP-based method and DC-programming-based method and achieves near-optimal performance.