New Optimized Solution Method for Beamforming in Cognitive Multicast Transmission

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.