Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks

In this paper, we explore intelligent reflecting surface (IRS)-assisted physical layer security (PLS) in a wireless-powered Internet of Things (IoT) network (WPIN) by combining an IRS, a friendly jammer, and energy harvesting (EH) to maximize sum secrecy throughput in the WPIN. Specifically, we propose a non-line-of-sight system where a hybrid access point (H-AP) has no direct link with the users, and a secure uplink transmission scheme utilizes the jammer to combat malicious eavesdroppers. The proposed scheme consists of two stages: wireless energy transfer (WET) on the downlink (DL) and wireless information transmission (WIT) on the uplink (UL). In the first phase, the H-AP sends energy to users and the jammer, and they then harvest energy with the help of the IRS. Consequently, during WIT, the user transmits information to the H-AP while the jammer emits signals to confuse the eavesdropper without interfering with the legitimate transmission. The phase-shift matrix of the IRS and the time allocation for DL and UL are jointly optimized to maximize the sum secrecy throughput of the network. To tackle the non-convex problem, an alternating optimization method is employed, and the problem is reformulated into two sub-problems. First, the IRS phase shift is solved using quantum particle swarm optimization (QPSO). Then, the time allocation for DL and UL are optimized using the bisection method. Simulation results demonstrate that the proposed method achieves significant performance improvements as compared to other baseline schemes. Specifically, for IRS elements N = 35, the proposed scheme achieves a throughput of 19.4 bps/Hz, which is 85% higher than the standard PSO approach and 143% higher than the fixed time, random phase (8 bps/Hz) approach. These results validate the proposed approach's effectiveness in improving network security and overall performance.