A trade-off strategy and correlation analysis for secrecy rate and power consumption in IRS-assisted cognitive radio networks

Abstract

This paper presents a novel approach to consider trade-off in secrecy rate and power consumption in intelligent reflecting surface (IRS)-assisted cognitive radio networks (CRNs). In CRNs, the objective of enhancing secrecy rate (SR) conflicts with that of reducing total power consumption (TPC), creating a trade-off that should be flexibly considered due to dynamic environmental requirements. Whereas IRS act as an essential role that helps enhance performance and facilitate trade-offs, but does not change the fundamental conflict between these two objectives. Investigating the relationship between environmental parameters and both SR and TPC is crucial for achieving optimal performance. To address this issue, we propose a weighting-based trade-off (WBTO) algorithm that leverages a multi-objective optimization framework to optimize SR and TPC simultaneously. Specifically, we formulate a nonconvex problem with coupled variables and use convex approximation methods such as the penalty function method and biconvex function difference method to solve two subproblems iteratively. Simulation results demonstrate that the proposed algorithm can effectively adjust the optimization trend by changing the weighting factor, achieving up to nearly a 1-fold performance improvement compared to the SR maximization algorithm and nearly half reduction in time consumption compared to another baseline algorithm. Our approach enables flexible adjustment of the weighting factor to efficiently adapt to new metrics and achieve optimal trade-offs between SR and TPC.