Discrete Phase Shift IRS-Assisted Energy Harvesting in Cognitive Radio Networks With Spectrum Sensing

The rapid growth in wireless device usage has intensified the demand for spectrum resources, leading to inefficiencies in traditional resource allocation methods. Cognitive Radio Networks (CRNs) address this by enabling secondary users (SUs) to access licensed spectrum bands of primary users (PUs) without compromising their Quality of Service (QoS). However, CRNs face challenges such as limited battery life and potential interference with PUs. Energy Harvesting (EH) techniques, particularly RF-based EH, offer a solution by powering CRN terminals, thereby enhancing spectrum utilization efficiency. Simultaneously, Intelligent Reflecting Surfaces (IRSs) have emerged as a powerful technology to enhance wireless propagation environments and support RF-based EH in CRNs. Despite this potential, most existing IRS-assisted CRN frameworks assume ideal continuous phase shifts, an impractical assumption given hardware limitations that permit only discrete phase levels, leading to quantization errors and increased design complexity. In this paper, we establish a unified system model for IRS-assisted Multiple Input Single Output (MISO) EH-CRNs that formulates an optimization problem to maximize SU throughput under practical constraints, including discrete IRS phase shifts, beamforming design, false alarm control, energy causality, and SU Quality of Service (QoS) requirements. To solve the non-convex problem, we develop a quantization-aware alternating optimization algorithm that decomposes the problem into interrelated subproblems for detection probability maximization, false alarm minimization, energy harvesting optimization, and SU throughput enhancement. Advanced techniques such as semidefinite relaxation (SDR), Successive Convex Approximation (SCA), and Nearest Point Search with Penalty (NPSP) are utilized to address practical implementation constraints. Simulation results demonstrate the superior performance of the proposed framework and the novel resource allocation algorithm based on alternating optimization. These results highlight the transformative potential of IRS with discrete phase shifts in enhancing EH-CRN efficiency, particularly in improving energy harvesting and SU throughput under practical constraints.