Data-driven ML Approaches for the concept of Self-healing in CWN, Including its Challenges and Possible Solutions

The idea of a self-organizing network, or SON for short, was developed to facilitate the autonomous deployment and administration of cellular networks. SON capabilities have the potential to improve service quality, increase network performance, and lower operating and capital expenditures (OPEX and CAPEX). Self-healing refers to a network paradigm in which faults in intended networks are repaired through the execution of a predetermined set of steps, and recompense. SON self-heals as a network paradigm. SON requires self-healing. It is now widely acknowledged that data-driven machine learning is an effective method for infusing networks with intelligence and enabling them to repair themselves. However, there are significant obstacles to overcome before practical implementations of machine-learning approaches for self-healing may be developed. In the first part of this essay, we will begin by dividing these difficulties into Data imbalance, data insufficiency, data cost insensitivity, data reaction time, multi-source data fusion, and data latency are the five main areas of concern. Then, after identifying these difficulties, we provide viable technological solutions to overcome them. In addition, A cost-sensitive defect detection case study employing unbalanced data shows the approaches' feasibility and efficiency

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