Transmit Beamforming in MIMO Cognitive Radio Network via Semidefinite Programming

Transmit beamforming is designed for multiple-input multiple-output (MIMO) cognitive radio network in which single primary user (PU) coexists with single secondary user (SU). The proposed cognitive beamforming minimizes the transmit power of the SU while limiting the interference temperature to PU and achieving the signal-to-interference-plus-noise ratio (SINR) target at SU. With perfect knowledge of channel information and receive beamforming pattern of the PU, the optimization problem is formulated as quadratically constrained quadratic programming (QCQP), and the optimal solution is obtained by using semidefinite programming relaxations (SDR). A probabilistic approach is developed by guaranteeing the interference temperature below a pre-specified threshold with high probability for the case when no knowledge of receive beamforming vector of the PU is available at the SU transmitter (SU-Tx). Simulation results demonstrate the effectiveness of the proposed approach in both scenarios.