### Related Work
Through simulations, it is demonstrated that these two methods outperform the conventional AB-aided overlay CRN and AB-aided underlay CRN. These CRNs do not use IRS in their communication chain. IRS-assisted URLLC networks with nonlinear energy harvesting are proposed for industrial automation [19]. A remote data centre communicates with multiple machines via the FD server and IRS. The server is assumed to be nearer to the machines in this case. IRS facilitates the reception of signals by the server from data centers, while nonlinear energy harvesting techniques specifically tackle the problem of insufficient transmit power from the server. The end-to-end performance of FD-IRS is examined in this work by deriving expressions for BLock Error Rate (BLER) for various scheduling algorithms. The performance of the suggested system is evaluated through the number of IRS elements, packet size, and different channel conditions. MC simulations are employed to validate the derived BLER expressions. IRS is used in [20] to fill coverage gaps in multiuser cellular FD links while also suppressing self-interference (SI) and co-channel interference. This approach allows BS and all users to exchange information at the same time, potentially doubling spectral efficiency. In this study, the BS precoding matrix and the IRS reflection coefficient are jointly optimized to maximize the minimum rate requirement of all users. In [21], active beamforming at sources and passive beamforming at IRS are jointly optimized to minimize total transmit power while maintaining minimum data rate requirements of sources. Semidefinite relaxation is used to obtain the active beamforming vectors. To solve the passive beamforming problem, successive convex approximation and semidefinite programming are used. Simulations show that the recommended FD-IRS achieves a higher performance gain than an IRS-assisted HD system. In [22], an IRS-aided IoT network operating in in-band FD mode is suggested. In this case, all IoT node pairs communicate at the same time and frequency via individual IBFD links. As a result, the nodes experience high SI and inter-node interference. Active beamforming at each node and passive beamforming at IRS are jointly optimized in this work to minimize total network transmit power while meeting the minimum rate requirement of each IoT node. The effect of IRS elements, nodes, and hardware flaws on system performance is also investigated. The performance of an IRS-aided multiuser FD secure communication system is examined in the presence of failures of the hardware at the transmitter, receiver, and IRS [23]. Deep reinforcement learning is employed for joint optimization at BS and IRS to maximize the sum secrecy rate. The IRS strengthens the secrecy against multiple eavesdroppers overhearing the two-way transmitted signals. The security vulnerabilities that could exist in a 6G communication network supported by IRS are described in [24]. In this work, meta-surface manipulation attacks which take place as a result of the manipulation of meta-surface behaviour are examined. In the context of Physical Layer security (PLS), two case studies involving information exploitation and information-gathering attacks are presented. These vulnerability cases are thoroughly investigated using BER and positive secrecy capacity metrics. Three transmission modes, namely direct, relay-aided, and IRS-aided communication, are addressed in the device-to-device (D2D) communication network [25]. The switching between different transmission modes is performed dynamically based on the network conditions and the availability of relays and IRS. The authors propose a switching algorithm that determines the optimal transmission mode based on the distance between devices, the signal strength, and the availability of relays and IRS. In [26], D2D unidirectional communication performance is enhanced by an iterative optimization algorithm for several parameters of the system, such as transmit power, IRS passive transmit beamforming, and BS receive beamforming.