Deep learning for 5G and 6G

Ardabili, Sina Faizollahzadeh; Mosavi, Amir; Felde, Imre

doi:10.1109/saci58269.2023.10158628

Cited by 1 publication

(1 citation statement)

References 48 publications

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“…To further increase capacity, 6G technology is also utilizing leadingedge technologies such as massive MIMO [74], mmWave technologies [75], T-Hz communication [76], VLC [77], and non-orthogonal multiple access (NOMA) [78]. Intelligent networks and services are also enabled by innovative AIbased techniques like machine learning [79], DL [80], and edge computing [81]. Upon utilizing predictive analytics, AI-RAN systems can preemptively deploy more base stations to locations with weak signal coverage.…”

Section: Network Capacity and Coverage In Ai-ran 6gmentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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6G is a next-generation cellular communication technology that builds up on existing 5G networks which are currently rolled out worldwide. Through incorporation of artificial intelligence (AI) and machine learning (ML), the core 5G network is advanced into an intelligent 6G network. The 6G Artificial Intelligence Radio Access Network (AI-RAN) is anticipated to offer advanced features like reduced latency, improved bandwidth, data rates and coverage. Furthermore, AI-RAN is expected to support complex use cases such as extreme connectivity, multi-user communications and dynamic spectrum access. This paper provides a detailed survey and thorough assessment of AI-RAN's vision and state-of-the-art challenges. We first present a concise introduction to 6G AI-RAN followed by background information on the current 5G RAN and its challenges that must be overcome to implement 6G AI-RAN. The paper then examines trending research issues in AI-RAN, i.e., challenges related to spectrum allocation, network architecture, and resource management. We discuss the methods to overcome these challenges which include the adoption of advanced machine learning and edge computing technologies to boost the performance of 6G AI-RAN. We conclude by stating open research directions.

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