Efficient Beamforming in Cognitive Radio Multicast Transmission

• Published in: IEEE transactions on wireless communications Vol. 11; no. 11; pp. 4108 - 4117
• Main Author: Beko, M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2012; Institute of Electrical and Electronics Engineers
• Subjects: Antennas; Applied sciences; Array signal processing; beamforming; Cognitive radio; convex-concave procedure; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Information, signal and communications theory; Interference; MIMO; Optimization; Radiocommunication specific techniques; Radiocommunications; Receivers; Robustness; second-order cone programming problem; Signal and communications theory; Signal to noise ratio; Signal, noise; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); Vectors; Performance evaluation; Second order; Parameter estimation; Non convex programming; convex-concave procedure; Iterative method; Data broadcast; Information dissemination; Beam forming; Multicast; Relaxation; Randomization; Channel estimation; Radio transmission; MIMO; Antenna array; Maximin problem; Cognitive radio; Algorithm; Computational complexity; Information transmission; second-order cone programming problem; Simulation; Minimax method; NP hard problem; Feasibility; beamforming; Service quality; Software radio
• ISSN: 1536-1276
• DOI: 10.1109/TWC.2012.092712.120201

The optimal beamforming problems for cognitive multicast transmission are quadratic nonconvex optimization problems. The standard approach is to convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation and later employ randomization techniques for solution search. However, in many cases, this approach brings solutions that are far from the optimal ones. We consider the problem of minimizing the total power transmitted by the antenna array subject to quality-of-service (QoS) at the secondary receivers and interference constraints at the primary receivers. It is shown that this problem, which is known to be nonconvex NP-hard, can be approximated by a convex second-order cone programming (SOCP) problem. Then, an iterative algorithm in which the SOCP approximation is successively improved is presented. Simulation results demonstrate the superior performance of the proposed approach in terms of total transmitted power and feasibility, together with a reduced computational complexity, as compared to the existing ones, for both the perfect and imperfect channel state information (CSI) cases. It is further shown that the proposed approach can be used to address the max-min fairness (MMF) based beamforming problem.