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When 5G began its commercialisation journey around 2020, the discussion onthe 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 a6G network will lead to an excessive number of automated decisions made inreal-time. These decisions can range widely, from network resource allocationto collision avoidance for self-driving cars. However, the risk of losingcontrol over decision-making may increase due to high-speed, data-intensive AIdecision-making beyond designers' and users' comprehension. The promisingexplainable AI (XAI) methods can mitigate such risks by enhancing thetransparency of the black-box AI decision-making process. This paper surveysthe application of XAI towards the upcoming 6G age in every aspect, including6G technologies (e.g., intelligent radio, zero-touch network management) and 6Guse cases (e.g., industry 5.0). Moreover, we summarised the lessons learnedfrom the recent attempts and outlined important research challenges in applyingXAI for 6G in the near future.

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