# Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks

## TL;DR

This paper proposes an iterative algorithm for joint beamforming and antenna selection in cognitive radio networks to maximize sum rate, effectively handling nonconvex constraints and outperforming existing methods.

## Contribution

A novel iterative algorithm using inner approximation for joint beamforming and antenna selection in cognitive radio networks, addressing nonconvex optimization challenges.

## Key findings

- Algorithm converges quickly in simulations.
- Outperforms existing approaches in sum rate maximization.
- Effectively handles nonconvex constraints.

## Abstract

This letter studies joint transmit beamforming and antenna selection at a secondary base station (BS) with multiple primary users (PUs) in an underlay cognitive radio multiple-input single-output broadcast channel. The objective is to maximize the sum rate subject to the secondary BS transmit power, minimum required rates for secondary users, and PUs' interference power constraints. The utility function of interest is nonconcave and the involved constraints are nonconvex, so this problem is hard to solve. Nevertheless, we propose a new iterative algorithm that finds local optima at the least. We use an inner approximation method to construct and solve a simple convex quadratic program of moderate dimension at each iteration of the proposed algorithm. Simulation results indicate that the proposed algorithm converges quickly and outperforms existing approaches.