Distributed Coordinated Beamforming for Multi-cell Multigroup Multicast Systems

TL;DR

This paper develops centralized and distributed algorithms for multi-cell multicast beamforming to minimize power while meeting user SINR targets, using convex relaxation and primal decomposition techniques.

Contribution

It introduces novel distributed beamforming algorithms for multi-cell multigroup systems based on convex approximation and primal decomposition methods.

Findings

Distributed algorithms require only local channel knowledge and scalar exchange.

The proposed algorithms outperform conventional schemes in numerical evaluations.

Optimality is achieved when the solution has unit rank, with Gaussian randomization used otherwise.

Abstract

This paper considers coordinated multicast beamforming in a multi-cell wireless network. Each multiantenna base station (BS) serves multiple groups of single antenna users by generating a single beam with common data per group. The aim is to minimize the sum power of BSs while satisfying user-specific SINR targets. We propose centralized and distributed multicast beamforming algorithms for multi-cell multigroup systems. The NP-hard multicast problem is tackled by approximating it as a convex problem using the standard semidefinite relaxation method. The resulting semidefinite program (SDP) can be solved via centralized processing if global channel knowledge is available. To allow a distributed implementation, the primal decomposition method is used to turn the SDP into two optimization levels. The higher level is in charge of optimizing inter-cell interference while the lower level optimizes beamformers for given inter-cell interference constraints. The distributed algorithm requires local channel knowledge at each BS and scalar information exchange between BSs. If the solution has unit rank, it is optimal for the original problem. Otherwise, the Gaussian randomization method is used to find a feasible solution. The superiority of the proposed algorithms over conventional schemes is demonstrated via numerical evaluation.