Robust Monotonic Optimization Framework for Multicell MISO Systems

TL;DR

This paper introduces a comprehensive optimization framework using a branch-reduce-and-bound algorithm to achieve global optimality in complex multicell MISO systems, accounting for channel uncertainty and various constraints.

Contribution

It presents a novel, systematic approach for globally optimizing resource allocation in multicell MISO systems, including a robust fairness-profile optimization method.

Findings

The BRB algorithm outperforms previous methods in convergence.

The framework can serve as a benchmark for multicell system optimization.

A zero-forcing solution is derived and evaluated within this framework.

Abstract

The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are non-convex and NP-hard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constraints. We establish a general optimization framework that systematically solves these problems to global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles general multicell downlink systems with single-antenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robustness to channel uncertainty. A robust fairness-profile optimization (RFO) problem is solved at each iteration, which is a quasi-convex problem and a novel generalization of max-min fairness. The BRB algorithm is computationally costly, but it shows better convergence than the previously proposed outer polyblock approximation algorithm. Our framework is suitable for computing benchmarks in general multicell systems with or without channel uncertainty. We illustrate this by deriving and evaluating a zero-forcing solution to the general problem.