  Non-orthogonal multiple access (NOMA) enabled fog radio access networks
(NOMA-F-RANs) have been taken as a promising enabler to release network
congestion, reduce delivery latency, and improve fog user equipments' (F-UEs')
quality of services (QoS). Nevertheless, the effectiveness of NOMA-F-RANs
highly 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 how
artificial intelligence (AI) techniques are utilized to solve foregoing
tremendous challenges. Specifically, we first elaborate on the NOMA-F-RANs
architecture, shedding light on the key modules, namely, cooperative caching
and cache-aided mobile edge computing (MEC). Then, the potentially applicable
AI-driven techniques in solving the principal issues of NOMA-F-RANs are
reviewed. Through case studies, we show the efficacy of AI-enabled methods in
terms of F-UEs' latent feature extraction and cooperative caching. Finally,
future trends of AI-driven NOMA-F-RANs, including open research issues and
challenges, are identified.
