Trustworthy artificial intelligence classification-based equivalent bandwidth control

Nowadays, machine learning (ML) is a viable solution for the allocation of equivalent bandwidth (EqB) in telecommunication networks, i.e. the minimum service rate required by a traffic buffer to guarantee a satisfactory Quality of Service (QoS). Moreover, trustworthy artificial intelligence (AI) is gaining importance in regulating the implementation of ML models, requiring explainable AI (XAI) and uncertainty handling.