Distributed Optimization for Coordinated Beamforming in Multi-Cell Multigroup Multicast Systems: Power Minimization and SINR Balancing
Oskari Tervo, Harri Pennanen, Dimitrios Christopoulos, Symeon, Chatzinotas, Bj\"orn Ottersten

TL;DR
This paper develops centralized and distributed algorithms for multi-cell multigroup multicast beamforming, optimizing power and SINR balancing, demonstrating improved performance and fast convergence over traditional methods.
Contribution
It introduces novel distributed beamforming algorithms based on primal decomposition and ADMM for power minimization and SINR balancing in multi-cell multicast systems.
Findings
Distributed algorithms converge quickly in simulations.
Proposed methods outperform non-coordinated schemes.
SDP-based solutions effectively approximate non-convex problems.
Abstract
This paper considers coordinated multicast beamforming in a multi-cell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the non-convex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, inter-cell interference powers are optimized for fixed…
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