Robust Multicast Beamforming for Spectrum Sharing-Based Cognitive Radios

• Published in: IEEE transactions on signal processing Vol. 60; no. 1; pp. 527 - 533
• Main Authors: Huang, Yongwei, Li, Qiang, Ma, Wing-Kin, Zhang, Shuzhong
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2012; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Approximation algorithms; Approximation methods; Array signal processing; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Imperfect channel state information; Information, signal and communications theory; Interference; MIMO cognitive radio networks; Quality of service; Receivers; robust multicast beamforming; Robustness; semidefinite programming relaxation; Signal and communications theory; Signal, noise; spectrum sharing; Telecommunications and information theory; Performance evaluation; Parameter estimation; Downlink; MIMO cognitive radio networks; Convex programming; Data broadcast; Information dissemination; Beam forming; Multicast; Relaxation; Channel estimation; Imperfect channel state information; spectrum sharing; MIMO system; robust multicast beamforming; Resource sharing; Quadratic programming; Algorithm; semidefinite programming relaxation; Constrained optimization; Simulation; Signal processing; Service quality; Radio communication; Software radio
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2011.2169061

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 robust design.