Efficient Beamforming Algorithm for Multi-Antenna Cognitive Radio Systems
Abstract: We propose and optimize a low-complexity precoding scheme for multiple antenna cognitive radio networks. In a cognitive network, a secondary transmitter is allowed to access the spectrum of the primary network only if the interference to the primary network remains below the predefined power limit. The proposed scheme, termed MSLNR, is a combination of the optimal minimum-mean-square-error (MMSE) receiver and the signal-to-leakage-plus-noise-ratio (SLNR) transmitter, with additional scaling to comply with the cognitive interference constraint. We also present a robust design method for the case where the secondary transmitter has only partial channel state information (CSI). The MSLNR scheme requires low implementation complexity. The transmit precoder is evaluated while taking into account the optimal receiver weight, but without any iterations. Yet, simulation results demonstrate that the performance of the proposed MSLNR scheme is close to the performance of the best known solution.
* The research presented in the seminar was carried our towards the M.Sc. degree in Electrical Engineering, with the supervision of Dr. Itsik Bergel