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
Conference Paper: Space-time beamforming for multiuser wireless relay networks.[Show abstract] [Hide abstract]
ABSTRACT: The paper is concerned with a multiuser commu- nication network, which is assisted by multiple relays. It has been observed through our previous related works that the conventional simultaneous beamforming at parallel amply-and- forward (AF) relays is not quite effective and often infeasible to target practically desirable signal-to-interference-and-noise ratio (SINR) at the destinations. To overcome this shortage, we propose the time-division for multiple-user transmission to the relays so the later can perform beamforming on signals received from the individuals and then parallelly forward its combinations at once to the destinations. The optimal beamforming problem is a non- convex quadratically constrained optimization, which is globally solved by our tailored algorithm of nonsmooth optimization. Its found global optimal solutions are shown very effective and over- perform other possible multi-user relay beamformings.Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011 · 4.63 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: It is known that the design of optimal transmit beamforming vectors for cognitive radio multicast transmission can be formulated as indefinite quadratic optimization programs. Given the challenges of such nonconvex problems, the conventional approach in literature is to recast them as convex semidefinite programs (SDPs) together with rank-one constraints. Then, these nonconvex and discontinuous constraints are dropped allowing for the realization of a pool of relaxed candidate solutions, from which various randomization techniques are utilized with the hope to recover the optimal solutions. However, it has been shown that such approach fails to deliver satisfactory outcomes in many practical settings, wherein the determined solutions are found to be unacceptably far from the actual optimality. On the contrary, we in this contribution tackle the aforementioned optimal beamforming problems differently by representing them as SDPs with additional reverse convex (but continuous) constraints. Nonsmooth optimization algorithms are then proposed to locate the optimal solutions of such design problems in an efficient manner. Our thorough numerical examples verify that the proposed algorithms offer almost global optimality whilst requiring relatively low computational load.IEEE Transactions on Signal Processing 01/2012; 60(6):2941-2951. · 2.81 Impact Factor