On the Need for Trustworthy Deep Learning Models for Efficient Resource Management in 6G Networks

The 6<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation (6G) of mobile networks are anticipated to provide significant benefits such as high data-rates, low-latency and seamless connectivity to a huge number of users. The realization of these networking potentialities is anchored in the innovative design and development of the networking operations with purpose to deal with complex environments in a efficient manner. In particular, principal networking operations such as the resource management will be mainly conducted with the aid of the Deep Learning (DL) models. The DL models are characterized by their efficiency in dealing with intense tasks but they lack of transparency and trust. For this reason, we propose a framework that incorporates both a methodology for uncertainty quantification (UQ) of the DL models and a trafficengineering model targeting on improving the trustworthiness in the resource allocation procedures. It is shown that the exploitation of trustworthy DL models significantly improves both the system's resource utilization and the system's service provisioning capability to the users.