Throughput Optimization for IRS-assisted Cognitive SWIPT Secondary User Networks

In order to improve the spectrum utilization efficiency and the energy limitation of cognitive simultaneous wireless information and power transfer (SWIPT) network, a study is conducted on an intelligent reflecting surface (IRS) -assisted cognitive SWIPT network, where the primary user network shares its spectrum with the secondary user network in overlay mode, the secondary transmitter simultaneously transmits energy to the primary transmitter and information to the secondary receiver. An optimization algorithm for the throughput of the secondary user network is proposed, under the constraints of the maximum transmit power of the secondary user transmitter, the minimum throughput requirement of the primary user network, the available time slots, and the phase shifts of the IRS, and the beamforming vector of the secondary transmitter, the time slot allocation, and the phase shifts of the IRS are jointly optimized to maximize the throughput of the secondary user network. The optimization variables of the proposed problem are coupled with each other and the structure is highly non-convex, making it is difficult to solve directly. The proposed algorithm applies alternating optimization, semi-positive relaxation, and successive convex approximation techniques to transform the original problem into three subproblems for alternative solution. Simulation results show that the proposed algorithm can significantly improve the throughput of the secondary user network compared with the existing benchmark schemes.