SINR Balancing in the Downlink of Cognitive Radio Networks with Imperfect Channel Knowledge
In this paper we consider the problem of signal-tointerference- plus-noise ratio (SINR) balancing in the downlink of cognitive radio (CR) networks while simultaneously keeping interference levels at primary user (PU) receivers (RXs) below an acceptable threshold with uncertain channel state information available at the CR base-station (BS). We optimize the beamforming vectors at the CR BS so that the worst user SINR is maximized and transmit power constraints at the CR BS and interference constraints at the PU RXs are satisfied. With uncertainties in the channel bounded by a Euclidean ball, the semidefinite program (SDP) modeling the balancing problem is solved using the recently developed convex iteration technique without relaxing the rank constraints. Numerical simulations are conducted to show the effectiveness of the proposed technique in comparison to known approximations.