Probability constrained robust multicast beamforming in cognitive radio network

In this paper, we design a probability constrained robust multicast beamforming in cognitive radio network. We adopt the Bernstein inequality to approximate the probability constraints into their corresponding deterministic forms. Since the approximate problem is still nonconvex, we then employ a successive convex approximation (SCA) technique to transfer it into an sequence SOCP and prove the SCA algorithm converges to a stationary point. Numerical simulations are provided to demonstrate the effectiveness of the proposed methods, and show its superiority compared with the conventional worst-case robust design.