Joint Radio Resource Management Soft Computing Technique for 5G and beyond 5G Wireless Networks

Radio Resource Management (RRM) has become more difficult due to the quick progress of 5G and the potential of 6G, necessitating creative solutions that go beyond conventional methods. To handle the dynamic and diverse character of next-generation wireless networks, this research provides a unique soft computing approach for Joint Radio Resource Management (JRRM). The suggested approach improves RRM by including flexibility, adaptability, and learning mechanisms while using the powers of soft computing, such as fuzzy logic, neural networks, and evolutionary algorithms for effective spectrum allocation. Additionally, 5G networks have greater, higher capacity, greater stability, and improved connectivity, in addition to faster speeds and lower latency. To meet the needs of various network applications, network connectivity has become important due to the increasing demand for data speeds, bandwidth capacity, and low latency. Soft computing-based assisted tools contribute to providing a higher and more intelligent level of network management. The need for resource optimization targets effective utilization of network resources, network energy efficiency and decreased operational expenditure in 5G and B5G networks using network slicing approach to handle multi-layer resources and to meet the increased demand of diverse quality of service requirements of next generation wireless networks. To mitigate the service specific requirements, Service-Driven Slice Management has been introduced. Comparing the simulation results to traditional RRM techniques, throughput, latency, and fairness are significantly improved. The work also examines how different system characteristics affect the JRRM approach's performance and talks about workable implementation techniques.