An adaptive IRS adjusting algorithm aids communication in heterogeneous cognitive radio networks

Abstract

In this paper, we present an adaptive intelligent reflecting surfaces (IRS) adjusting algorithm designed to enhance the communication quality of secondary users (SUs) within heterogeneous cognitive radio networks (CRNs), using a cross-layer analysis approach. Initially, a Markov model is established based on queue analysis of SUs' buffer. Subsequently, to optimize the diagonal reflection coefficient matrix of the IRS, we derive the key objectives of the established multi-objective optimization problem, including potential throughput, data packet rejection rate, and data packet queue length of SUs, into closed-form expressions. Thereafter, the optimal solution guides the dynamic pre-adjustment of IRS by the station. Simulation results verify the superior performance of the proposed algorithm, particularly in terms of spectral efficiency and bit error rate, compared to existing methods.