Joint Probabilistic Constrained Robust Beamforming and Antenna Selection
A novel method is proposed for joint probabilistic constrained robust beamforming and antenna selection used in cognitive radio networks. Assuming complex Gaussian distributed channel state information errors, the Bernstein-type inequalities are introduced to transform no closed-form probabilistic constrained forms into the deterministic forms. Moreover, the ?1-norm is used as the closest convex approximation of ?0-norm. Thus the original NP-hard optimal problem can be relaxed as a tractable convex optimization problem. A computationally efficient and near-optimal solution is obtained by an iteratively re-weighted algorithm. Simulations show that the proposed algorithm satisfies the predetermined service levels at relatively small excess transmission power in a number of transmitter reduction scenarios.
probabilistic constrained robust beamforming antenna selection