The evolution of telecommunications technology is at the cusp of a major transition from 5G to beyond 5G (B5G) networks. With this imminent shift, the demand for robust and efficient mitigation solutions has become increasingly vital. AI-based mitigation solutions for solving B5G network problems are directly pushed into the actual field or manually evaluated by the operator first. With Big Data involved in 5G and Beyond, evaluating them manually or without evaluation, pushing them into the real field might have severe consequences in the actual network. There is no intelligent and proactive platform to test the implications of ML models on the networks. In this paper, we propose a pioneering approach that involves the development of an AI-based 5G network clone to serve as a performance evaluation ground for AI-based mitigation solutions tailored for B5G networks. Our methodology outlines the initial phase of evaluating these mitigation solutions within the simulated environment of the AI-based 5G network clone, followed by their subsequent deployment in real-world network infrastructures. This strategy aims to ascertain the efficacy, reliability, and adaptability of the proposed solutions before their integration into the next-generation B5G networks.

https://ieeexplore.ieee.org/document/10622702