Energy-Efficient Optimization of Cooperative Spectrum Sensing Algorithms in Multi-RAT Cognitive Networks

Abstract

The growing demand for wireless services necessitates efficient spectrum use, especially in 5G, NextG, and Internet of Things (IoT) network scenarios. Cognitive Radio (CR) systems can be employed to detect underutilized bands, allowing Secondary Users (SUs) to opportunistically access these bands, thus improving spectrum efficiency. Cooperative Spectrum Sensing (CSS) is essential for reliable detection of Primary Users (PUs), but full SU participation is energy-intensive. This article presents an energy-efficient sensing optimization algorithm that dynamically selects an optimal SU subset by iteratively assessing marginal gains in detection probability. SUs are ranked by Signal-to-Noise Ratio (SNR), and an iterative loop with a convergence threshold determines the final sensing set. Simulations show the algorithm significantly reduces the number of active SUs, still obtaining excellent detection performance.