Robust Downlink Beamforming in Multiuser MISO Cognitive Radio Networks

TL;DR

This paper develops robust downlink beamforming strategies for multiuser MISO cognitive radio networks with imperfect channel information, aiming to minimize power while ensuring quality of service and limiting interference.

Contribution

It introduces three convex programming approaches for robust beamforming under channel uncertainty modeled by Euclidean balls, enhancing reliability in cognitive radio networks.

Findings

Proposed methods achieve power minimization with guaranteed SINR levels.

Robust designs maintain performance despite channel estimation errors.

Simulation results validate the effectiveness of the approaches.

Abstract

This paper studies the problem of robust downlink beamforming design in a multiuser Multi-Input Single-Output (MISO) Cognitive Radio Network (CR-Net) in which multiple Primary Users (PUs) coexist with multiple Secondary Users (SUs). Unlike conventional designs in CR-Nets, in this paper it is assumed that the Channel State Information (CSI) for all relevant channels is imperfectly known, and the imperfectness of the CSI is modeled using an Euclidean ball-shaped uncertainty set. Our design objective is to minimize the transmit power of the SU-Transmitter (SU-Tx) while simultaneously targeting a lower bound on the received Signal-to-Interference-plus-Noise-Ratio (SINR) for the SU's, and imposing an upper limit on the Interference-Power (IP) at the PUs. The design parameters at the SU-Tx are the beamforming weights, i.e. the precoder matrix. The proposed methodology is based on a worst case design scenario through which the performance metrics of the design are immune to variations in the channels. We propose three approaches based on convex programming for which efficient numerical solutions exist. Finally, simulation results are provided to validate the robustness of the proposed methods.