Optimized Dynamic Spectrum Sharing for Cognitive Radio with Full-Duplex Primary Users, Enhancing 5G Networks' Spectral Efficiency by Mitigating Self-Interference

In this research, a customized and effective Dynamic Spectrum Sharing (DSS) approach is proposed for use in Cognitive Radio (CR) systems to handle challenges brought by In-Band Full-Duplex (IBFD) Primary User (PU) networks in 5G environments. Since more data is being used, the existing wireless networks are under more stress, so using the spectrum efficiently is very important. Most traditional methods are unable to handle changing conditions and interference situations, especially with IBFD systems, because of their interference issues. This new framework reduces self-interference, enhancing spectrum access opportunities for both PUs and SUs. With this approach, the parameters for sharing the radio spectrum are adjusted in real time by the control function, which improves decisions and system reliability. The method designs its system so that primary users apply proper Gaussian signalling, whereas secondary users make use of improper Gaussian signals to send communications. This design prevents disruption between PUs and SUs, and the bandwidth is used well with little or no interference. Because cognitive radios have to be careful not to disturb the PUs, the right signalling approach provides more protection against interference, making the system both stronger and more efficient in terms of spectrum usage. A new game model is introduced, and it relies on enthalpy-based sigmoid functions to dynamically change and monitor the DSS parameters automatically. You can use these algorithms in real-time, and they need less computing power and cost less, which is needed in 5G networks. Also, as a new development, an Improved Multi-Objective Grasshopper optimisation algorithm (I-MOGOA) is provided, which surpasses the Genetic Algorithm (GA) and Simplified Particle Genetic Algorithm (SPGA) in delivering enhanced spectral efficiency and energy conservation. It is notable that I-MOGOA manages to keep several performance metrics under control, such as Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), throughput, transmission power and Signal-To-Self-Interference-Plus-Noise Ratio (SSINR). Analytics have proven that this method substantially impacts all of the performance indicators. Both the system flowchart and simulation studies indicate that higher SUs can lead to a drop in spectral efficiency caused by more interference and fewer resources, highlighting the essential balance in the allocated spectrum. Because of the sigmoid-based control, the system continues to adjust resource access in real time, leading to fair and stable sharing between the primary and secondary users. It has been found in this research that the SNR decreases while the spectral efficiency increases in 5G cognitive radio systems. While a high SNR gives better signal quality, it can make the network use more energy and simultaneously reduce the number of bits sent through the same channel. Therefore, careful handling of this tradeoff is needed. The suggested method can be used to find the right SNR level to achieve the best tradeoff between using less power and getting higher throughput. The study shows that bad SNR management causes higher BER, lower throughput, and unnecessary energy use. The new system is superior to regular IBFD and interference reduction methods through experiments that achieve strong BER, reliable signals, and safe transmission. It proves that grouping, advanced signal modelling, optimization algorithms, and real-time interference management improve performance. The fact that the new DSS framework is more effective than traditional methods means it helps 5G networks today and sets the stage for progress in cognitive radio technology later. The research introduces a DSS model that can adjust and grow, setting a good start for the development of wireless systems. It fixes major shortcomings in the current CR by using good methods to share the radio spectrum, not getting stuck in densely used networks or persistent interference. In brief, the suggested enthalpy-based sigmoid DSS method ensures high performance, energy saving and resistance to interference in 5G-based cognitive radio systems. Integrating dynamic control, signal optimization, and evolutionary algorithms plays a key role in this research, which delivers an important solution for next-generation wireless communications. It improves the efficiency and dependability of the system and prepares for the use of new wireless services in the future.