New Optimized Solution Method for Beamforming in Cognitive Multicast Transmission
ABSTRACT The optimal beamforming for cognitive multicast transmission is nonconvex rank-one constrained optimization problem. For a solution, a popular method is the combination of relaxed convex semi-definite programming, where the rank-one constraint is dropped, and randomization. We show that in many cases, this method cannot give satisfactory solutions. As an initial step, we develop a simple alternative method, which gives much better solutions. Our simulation confirms this fact.
Conference Paper: D.C. iterations for SINR maximin multicasting in Cognitive radio[Show abstract] [Hide abstract]
ABSTRACT: The design of transmit beamforming vectors to maximize the threshold of the signal-to-interference-plus-noise ratios (SINR) at the secondary receivers in cognitive multicast transmission is maximin optimization of quadratic fractional functions. There is no efficient solver for this hard maximin program. In the present paper, we show that the program can be effectively represented by a canonical d.c. (difference of convex functions) program of the same size. Accordingly, d.c. iterations are derived to locate its optimized solution. Our thorough numerical examples verify that the proposed algorithms offer almost global optimality whilst requiring relatively low computational load.Signal Processing and Communication Systems (ICSPCS), 2012 6th International Conference on; 01/2012
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ABSTRACT: In this paper, a novel algorithm for transmit beamforming to single cochannel multicast group is presented. The problem of minimizing the total power transmitted by the antenna array subject to interference constraints at the primary receivers and quality-of-service (QoS) constraints at the secondary receivers is addressed. It is shown that this problem, which is 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 refined is proposed. Simulation results show the superior performance of the proposed approach in terms of the total beamforming power, feasibility and computational complexity as compared to the existing ones1.Communications (ICC), 2012 IEEE International Conference on; 01/2012
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ABSTRACT: 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.IEEE Transactions on Wireless Communications 11/2012; 11(11):4108-4117. DOI:10.1109/TWC.2012.092712.120201 · 2.76 Impact Factor