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

TL;DR

This paper reviews how AI techniques enhance NOMA-F-RANs by improving feature extraction, caching, and resource management, aiming to reduce latency and improve QoS in fog radio networks.

Contribution

It provides a comprehensive overview of AI applications in NOMA-F-RANs, including architecture, key modules, and case studies demonstrating AI's effectiveness in feature extraction and caching strategies.

Findings

AI improves feature extraction for F-UEs

AI enhances cooperative caching efficiency

Future challenges in AI-driven NOMA-F-RANs are identified

Abstract

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.