# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
### Related Work
#### 1.1.2 IRS-Assisted Wireless Networks  
The intelligent reflecting surface (IRS) has emerged as a key technology in smart and reconfigurable radio environments, offering a promising approach to boosting the performance of wireless networks. This can be achieved without requiring an additional RF spectrum or increasing energy consumption, making it a critical element for enhancing the sustainability of next-generation networks [17]. The IRS consists of a large array of passive reflecting elements and a smart controller [18]. It is possible to dynamically adjust each IRS component to change the wireless propagation environment. As a result, IRSs are typically deployed to manipulate the scattering, reflection, and refraction of radio waves, mitigating the adverse impacts of unpredictable wireless environments.  
Recent studies in IRS-aided wireless networks have focused on improving system throughput and energy efficiency [19; 20; 21]. [PERSON] et al. conducted a study on an IRS-assisted system [19] wherein the IRS reflects energy signals from a PS to enable energy harvesting for IoT devices. Once the devices harvest sufficient energy, they transmit to an AP using a time division multiple-access (TDMA) scheme with the assistance of the IRS. By setting up the IRS phase shift for uplink WIT and downstream WET, and by scheduling transmission time slots optimally, system performance was enhanced, and cumulative throughput maximized. The work in [20; 21] maximized EE while minimizing total transmission power by utilizing an IRS in a downlink multiple-input single-output (MISO) system where active and passive beamformers are optimized using an alternating optimization (AO) method. In [24], [PERSON] and [PERSON] proposed a hybrid active-passive, IRS-assisted wireless-powered communication network (WPCN) to significantly enhance network capacity by leveraging both active and passive reflecting elements. With this, the authors optimized WET and WIT by employing a divide-and-conquer methodology to solve the complex, non-convex optimization problem. In [26], [PERSON] et al. conducted a study on IRS-assisted WPINs. They specifically examined scenarios involving multiple IoT devices that first harvest energy from a PS. Then, considering frequency division multiple access (FDMA), each IoT device utilizes the harvested energy to send information to an AP on uplink. The IRS enhances WET and WIT capabilities by utilizing passive reflection beamformers. The authors in [27] proposed a multi-cluster WPIN assisted by multiple IRSs. Within this network, dispersed IoT devices arranged into clusters are first powered by wireless energy broadcasts from a PS. Then, using a hybrid TDMA-FDMA protocol, the devices utilize the harvested energy to send data to an AP. Additionally, multiple IRSs are employed to perform anomalous reflection, enhancing both energy harvesting and data transmission capabilities. The notations used for mathematical expressions are shown in Table 1.  
In [28], [PERSON] et al. suggested utilizing artificial intelligence (AI) methods in IRS-assisted EE communications systems intended for 6G IoT networks. In [29], [PERSON] et al. proposed a non-orthogonal multiple-access (NOMA) technique within an IRS-aided WPCN, where passive beamforming of the IRS was optimized to enhance network throughput. In [31], [PERSON] et al. considered an approach similar to multi-cluster scenarios where numerous IoT devices are arranged into various clusters, and TDMA is used by each cluster. Using NOMA, the IoT devices share a sub-time slot for information transmission within each cluster. That study showed that the hybrid TDMA-NOMA protocol could effectively balance performance and complexity, offering a practical solution for enhancing system efficiency.