Abstract

Fifth-generation (5G) and beyond networks various emerging applications such as AR/VR/XR, e-Health, live video streaming, and automated vehicles are expected to have diverse and strict quality of service (QoS) requirements. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. It enables the creation of multiple virtual networks, each operating on a shared physical infrastructure, to meet various application requirements. This approach allows for customized network environments tailored to specific needs, such as different Quality of Service (QoS) levels, security protocols, and performance characteristics. This paper also envisages the usage of Explainable AI which plays a significant role in making machine learning models more transparent and understandable. In the context of network slicing and other telecom applications which enhance the interpretability and trustworthiness of machine learning models. This is essential for effective decision making and maintaining high service standards.

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