Robust Multicast Beamforming for Spectrum Sharing-Based Cognitive Radios

Abstract—We consider a robust downlink beamforming optimization problem for secondary multicast transmission in a multiple-input multiple-output (MIMO) spectrum sharing cognitive radio (CR) network. The minimization of transmit power is formulated subject to both quality-of-service (QoS) constraints on the secondary receivers and interference temperature constraints on the primary users, under the assumption of imperfect channel state information (CSI). The problem is a nonconvex quadratically constrained quadratic program (QCQP), and in general it is hard to achieve the global optimality. As a compromise, we present two randomized approximation algorithms for the problem via convex optimization techniques. Apart from the general setting of the robust beamforming problem, we identify one interesting special case, the robust problem of which can be solved efficiently. Simulation results are presented to demonstrate the performance gains of the proposed algorithms over an existing rob...

• Type: Journal
• Year: 2012
• Where: TSP
• Authors: Yongwei Huang, Qiang Li, Wing-Kin Ma, Shuzhong Zhang
• Keywords: Approximation Algorithms, Artificial Intelligence, Convex Optimization Techniques, Downlink Beamforming, TSP 2012