The primary aim of a cognitive radio (CR) system is to optimize spectrum usage by exploiting the existing spectrum holes. Nevertheless, the success of cognitive radio technology is significantly threatened by the primary user emulation attack (PUEA). A rogue secondary user (SU) known as the primary user emulator (PUE) impersonates a legitimate primary user (PU) in a PUEA, thereby preventing other SUs from accessing the spectrum holes. Which leads to the decrease in quality of service (QoS), connection undependability, degraded throughput, energy depletion, and the network experiences a deterioration in its overall performance. In order to alleviate the impact of PUEA on Cognitive Radio Networks (CRNs), it is necessary to detect and isolate the threat agent (PUE) from the network. In this paper, a method for finding and isolating the PUE is proposed. MATLAB simulation results showed that the presence of PUE caused a significant decrease in the throughput of SUs, from to . The throughput was highest at a false alarm (FA) probability of 0.0, indicating no PUE, and decreased as the FA probability increased. At a FA probability of 1, the throughput reached zero, indicating complete takeover of the spectrum by PUE. By isolating the PUE from the network, the other SUs can access the spectrum holes, leading to increased QoS, connection reliability, improved throughput, and efficient energy usage. The presented technique is an important step towards enhancing the security and reliability of CRNs.

When interference is reduced, the benefits of using a macrocell and femtocell heterogeneous network (Macro-Femto) heterogeneous network (HetNet) can be increased to their full potential. In this study, Enhanced Active Power Control (EAPC), Active Power Control (APC), and Power Control (PC1) interference mitigation strategies are applied, and their performances in uplink and downlink transmission of 5G Non-Stand-Alone (NSA) architecture are compared. According to the findings of a MATLAB simulation, the EAPC technique utilized a lower amount of transmit power for the Macro User Equipment (MUE), the Home User Equipment (HUE), and the femtocell logical node (Hen-gNB), in comparison to the APC and PC1 techniques. While PC1 approach required less en-gNB transmission power. The MUE, HUE, hen-gNB, and en-gNB throughput of the EAPC approach was much higher. This work will enable wireless system designers and network engineers know the appropriate technique to utilize to achieve desired Quality of Service (QoS) while conserving network resources.