Applications of explainable AI for 6G: Technical aspects, use cases, and research challenges

S Wang, MA Qureshi, L Miralles-Pechuan… - arxiv preprint arxiv …, 2021 - arxiv.org
When 5G began its commercialisation journey around 2020, the discussion on the vision of
6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability …

Federated learning-empowered mobile network management for 5G and beyond networks: From access to core

J Lee, F Solat, TY Kim, HV Poor - … Communications Surveys & …, 2024 - ieeexplore.ieee.org
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …

A survey on explainable ai for 6g o-ran: Architecture, use cases, challenges and research directions

B Brik, H Chergui, L Zanzi, F Devoti, A Ksentini… - arxiv preprint arxiv …, 2023 - arxiv.org
The recent O-RAN specifications promote the evolution of RAN architecture by function
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …

Improving performance, reliability, and feasibility in multimodal multitask traffic classification with XAI

A Nascita, A Montieri, G Aceto… - … on Network and …, 2023 - ieeexplore.ieee.org
The promise of Deep Learning (DL) in solving hard problems such as network Traffic
Classification (TC) is being held back by the severe lack of transparency and explainability …

Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research

B Brik, H Chergui, L Zanzi, F Devoti… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The recent o-ran specifications promote the evolution of ranran architecture by function
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …

A Survey on Explainable Artificial Intelligence for Internet Traffic Classification and Prediction, and Intrusion Detection

A Nascita, G Aceto, D Ciuonzo… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
With the increasing complexity and scale of modern networks, the demand for transparent
and interpretable Artificial Intelligence (AI) models has surged. This survey comprehensively …

Toward native explainable and robust AI in 6G networks: Current state, challenges and road ahead

C Fiandrino, G Attanasio, M Fiore, J Widmer - Computer Communications, 2022 - Elsevier
Abstract 6G networks are expected to face the daunting task of providing support to a set of
extremely diverse services, each more demanding than those of previous generation …

Interpreting AI for networking: Where we are and where we are going

T Zhang, H Qiu, M Mellia, Y Li, H Li… - IEEE Communications …, 2022 - ieeexplore.ieee.org
In recent years, artificial intelligence (AI) techniques have been increasingly adopted to
tackle networking problems. Although AI algorithms can deliver high-quality solutions, most …

Towards explainable artificial intelligence in optical networks: the use case of lightpath QoT estimation

O Ayoub, S Troia, D Andreoletti… - Journal of Optical …, 2022 - ieeexplore.ieee.org
Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial
capabilities in solving a wide range of optical-network-related tasks such as fault …

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

A Donatti, SL Correa, JSB Martins… - … on Network and …, 2023 - ieeexplore.ieee.org
Network slicing (NS) is becoming an essential element of service management and
orchestration in communication networks, starting from mobile cellular networks and …