ORAN-B5G: A Next-Generation Open Radio Access Network Architecture With Machine Learning for Beyond 5G in Industrial 5.0
Abdullah Ayub Khan, Asif Ali Laghari, Abdullah M. Baqasah, Roobaea Alroobaea, Thippa Reddy Gadekallu, Gabriel Avelino Sampedro, Yaodong Zhu
Abstract
Autonomous decision-making is considered an intercommunication use case that needs to be addressed when integrating open radio access networks with mobile-based 5G communication. The robustness of innovations is diminished by the conventional method of designing an end-to-end radio access network solution. Through an analysis of these possibilities, this paper presents a machine learning-based intelligent system whose primary goal is load balancing using Artificial Neural Networks with Particle Swam Optimization-enabled metaheuristic optimization mechanisms for telecommunication industry requests, like product compatibility. We increase the proposed system’s reliability by using third-generation partnership project standards to automate the distribution of transactional load among various connected units. This intelligent system encloses the hierarchy of automation enabled by artificial intelligence. Conversely, AI-enabled open radio access control explores the barriers to next-generation intercommunication, including those after 5G. It covers deterministic latency and capabilities, physical layer-based dynamic controls, privacy and security, and testing applications for AI-based controller designs.