Robust interference control techniques for cognitive radios using worst-case performance optimization

Cognitive radio networks have the ability to efficiently utilize the radio spectrum by allowing unlicensed users to communicate in the licensed frequency bands. Transmit beamformers can be designed by setting constraints on the interference temperature of the licensed users and signal to interference and noise ratios (SINRs) of the cognitive users. This design is however very sensitive to errors in channel state information (CSI). In this paper, we propose robust beamforming techniques for cognitive radios using worst-case performance optimization. The proposed beamformer has the ability to control the interference leaked to licensed users below a target value for all possible errors in the CSI. The problem is formulated within a convex optimization framework with constraints on worst-case errors. The performance of the robust beamformer is compared with non-robust beamformer in terms of bit error rates of the licensed users and probability density function of the interference power.