Efficient Network Resource Management for Improving Radio Access Through Machine Learning Approach in 5G Networks

Network resource management is one of the vital factors in recent dynamic technological computing paradigm as there is a significant degradtion in resource utilization. Network resources include networking, management systems and management support organizations. The major roles are to manage tasks, schedule resources and manage networks. It needs new challenges and requirements, making it more difficult to accomplish the intended objectives. Further, it involves complexity for mobile operators to devise solutions that are flexible, dynamic, cost-effective and intelligent with conventional wireless network. These issues can be addressed by emerging services of 5G (5th Generation) technology incorporating machine learning technique to enhance the performance within a real-time network.The work in this paper highlights the overview of the requirements, key technologies, and challenges associated with 5G cellular networks. Further machine learning techniques like long short term memory (LSTM), deep belief network (DBN), hybrid genetic algorithm (HGA) are incorporated in 5G networks and addressing 5G’s challenges using machine learning. The proposed work employing machine learning algorithms will show that the performances of resource management is superior than existing methods.