An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

TL;DR

This paper proposes an efficient precoder design for multiuser MIMO cognitive radio networks that maximizes sum rate under interference and power constraints, using a novel convex optimization approach with fast convergence.

Contribution

It introduces a new convex optimization algorithm for precoder design in multiuser MIMO cognitive radio systems with interference constraints, improving computational efficiency.

Findings

The proposed algorithm converges quickly in simulations.

It effectively manages interference constraints at primary users.

The method outperforms existing approaches in computational speed.

Abstract

We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output broadcast channel, where the secondary system consisting of a secondary base-station (BS) and a group of secondary users (SUs) is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm.