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Non-orthogonal multiple access (NOMA) enabled fog radio access networks(NOMA-F-RANs) have been taken as a promising enabler to release networkcongestion, reduce delivery latency, and improve fog user equipments' (F-UEs')quality of services (QoS). Nevertheless, the effectiveness of NOMA-F-RANshighly relies on the charted feature information (preference distribution,positions, mobilities, etc.) of F-UEs as well as the effective caching,computing, and resource allocation strategies. In this article, we explore howartificial intelligence (AI) techniques are utilized to solve foregoingtremendous challenges. Specifically, we first elaborate on the NOMA-F-RANsarchitecture, shedding light on the key modules, namely, cooperative cachingand cache-aided mobile edge computing (MEC). Then, the potentially applicableAI-driven techniques in solving the principal issues of NOMA-F-RANs arereviewed. Through case studies, we show the efficacy of AI-enabled methods interms of F-UEs' latent feature extraction and cooperative caching. Finally,future trends of AI-driven NOMA-F-RANs, including open research issues andchallenges, are identified.

A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing