Spectrum Sensing Optimization Strategy of IRS Assisted SIMO-MAC Cognitive Radio System

‍ ‍Cognitive radio （CR） was of crucial importance in improving the spectral efficiency of wireless communications systems by allowing the secondary users （SU） to share the spectrum with the primary user （PU）. Recently， the proposal of Intelligent Reflecting Surface （IRS） allowed us to reconstruct the channel environment through IRS reflective elements to improve the performance of wireless communication systems. In this paper， we proposed an IRS-assisted single input multiple output multiple access channels （SIMO-MAC） CR network. Moreover， we proposed a novel IRS-assisted cooperative spectrum sensing （CSS） scheme to improve the sensing performance. We formulated the sum rate maximization problem by jointly optimizing the power allocation of secondary users and the phase shifts of IRS， subjected to the power constraint of the secondary user and the interference constraint to the primary user. In order to tackle the non-convex problem with couple variables， we exploited an efficient alternating optimization algorithm based on block coordinate descent. Firstly， the optimal power allocation was obtained by adopting the Lagrange multiplier method， the water injection power allocation and Karush-Kuhn-Tucker （KKT） condition. Secondly， the multi-objective optimization problem was constructed， and the non-convex problem was transformed into a convex problem of semi-positive definite programming by using successive convex approximation （SCA） and semi-definite relaxation （SDR）， so as to solve the approximate optimal solution of the IRS phase shift matrix. Simulation results show that our proposed scheme can greatly improve spectral sensing performance and the spectrum efficiency of the Cognitive radio network.