Toward Explainable Reasoning in 6G: A Proof of Concept Study on Radio Resource Allocation
Published in Unknown Venue, 2024
Recommended citation: Farhad Rezazadeh and Sergio Barrachina-Muñoz and Hatim Chergui and Josep Mangues and Mehdi Bennis and Dusit Niyato and Houbing Song and Lingjia Liu (2024). Toward Explainable Reasoning in 6G: A Proof of Concept Study on Radio Resource Allocation. Unknown Venue. https://ieeexplore.ieee.org/abstract/document/10689363/
Abstract: The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put more emphasis on the importance of explainability and trustworthiness in network management operations, especially for mission-critical use-cases. Such desired trust transcends traditional post-hoc explainable AI (XAI) methods to using contextual explanations for guiding the learning process in an in-hoc way. This paper proposes a novel graph reinforcement learning (GRL) framework named TANGO which relies on a symbolic subsystem. It consists of a Bayesian-graph neural network (GNN) Explainer, whose outputs, in terms of edge/node importance and uncertainty, are periodically translated to a logical GRL reward function. This adjustment is accomplished through defined symbolic reasoning rules within a Reasoner. Considering a real-world testbed proof-of-concept (PoC), a gNodeB (gNB) radio resource allocation …