Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

TL;DR

This paper surveys how explainable AI can enhance transparency and trust in 6G networks, addressing technical aspects, use cases, and research challenges for future AI-driven communication systems.

Contribution

It provides a comprehensive overview of XAI applications in 6G, highlighting recent developments, lessons learned, and outlining key research challenges.

Findings

XAI can improve transparency in 6G AI decision-making

Identified critical research challenges for applying XAI in 6G

Summarized lessons from recent XAI implementations in 6G

Abstract

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, energy efficiency, lower latency, and an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made in real-time. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed, data-intensive AI decision-making beyond designers' and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black-box AI decision-making process. This paper surveys the application of XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for 6G in the near future.