Multicell Coordinated Beamforming with Rate Outage Constraint--Part II: Efficient Approximation Algorithms

TL;DR

This paper develops efficient, suboptimal algorithms for coordinated beamforming in interference channels with rate outage constraints, improving computational efficiency and performance over existing methods.

Contribution

It introduces two novel distributed algorithms based on BSUM and WMMSE for outage-constrained beamforming, along with a benchmark approximation method.

Findings

Algorithms outperform existing methods in performance and efficiency

Distributed WMMSE achieves parallel optimization of sum rate

Proposed methods yield promising approximation accuracy

Abstract

This paper studies the coordinated beamforming (CoBF) design for the multiple-input single-output interference channel, provided that only channel distribution information is known to the transmitters. The problem under consideration is a probabilistically constrained optimization problem which maximizes a predefined system utility subject to constraints on rate outage probability and power budget of each transmitter. Our recent analysis has shown that the outage-constrained CoBF problem is intricately difficult, e.g., NP-hard. Therefore, the focus of this paper is on suboptimal but computationally efficient algorithms. Specifically, by leveraging on the block successive upper bound minimization (BSUM) method in optimization, we propose a Gauss-Seidel type algorithm, called distributed BSUM algorithm, which can handle differentiable, monotone and concave system utilities. By exploiting a weighted minimum mean-square error (WMMSE) reformulation, we further propose a Jocobi-type algorithm, called distributed WMMSE algorithm, which can optimize the weighted sum rate utility in a fully parallel manner. To provide a performance benchmark, a relaxed approximation method based on polyblock outer approximation is also proposed. Simulation results show that the proposed algorithms are significantly superior to the existing successive convex approximation method in both performance and computational efficiency, and can yield promising approximation performance.