Weighted Sum Rate Optimization for Cognitive Radio MIMO Broadcast Channels

TL;DR

This paper addresses weighted sum rate maximization in cognitive radio MIMO broadcast channels with interference constraints, proposing a novel duality approach and an efficient algorithm that guarantees global optimality.

Contribution

It introduces a duality framework for MIMO-BC with multiple linear constraints and develops a convergent iterative algorithm for optimal resource allocation.

Findings

The proposed algorithm converges to the global optimum.

Simulation results validate the effectiveness of the algorithm.

The duality extends existing results to multiple constraints.

Abstract

In this paper, we consider a cognitive radio (CR) network in which the unlicensed (secondary) users are allowed to concurrently access the spectrum allocated to the licensed (primary) users provided that their interference to the primary users (PUs) satisfies certain constraints. We study a weighted sum rate maximization problem for the secondary user (SU) multiple input multiple output (MIMO) broadcast channel (BC), in which the SUs have not only the sum power constraint but also interference constraints. We first transform this multi-constraint maximization problem into its equivalent form, which involves a single constraint with multiple auxiliary variables. Fixing these multiple auxiliary variables, we propose a duality result for the equivalent problem. Our duality result can solve the optimization problem for MIMO-BC with multiple linear constraints, and thus can be viewed as an extension of the conventional results, which rely crucially on a single sum power constraint. Furthermore, we develop an efficient sub-gradient based iterative algorithm to solve the equivalent problem and show that the developed algorithm converges to a globally optimal solution. Simulation results are further provided to corroborate the effectiveness of the proposed algorithm.