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

• Published in: IEEE transactions on signal processing Vol. 63; no. 11; pp. 2763 - 2778
• Main Authors: Li, Wei-Chiang, Chang, Tsung-Hui, Chi, Chong-Yung
• Format: Journal Article
• Language: English
• Published: IEEE 01.06.2015
• Subjects: Approximation algorithms; Approximation methods; Array signal processing; Convex optimization; coordinated beamforming (CoBF); Interference; interference channel; outage probability; Receivers; Signal processing algorithms; Transmitters
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2015.2414896

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. Both algorithms are shown to converge to the stationary points of the original NP-hard problems. To further 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 by comparing with the performance benchmark.