Resource allocation in 5G cloud-RAN using deep reinforcement learning algorithms: A review
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper reviews recent research on resource allocation in 5G cloud-based radio access networks (C-RAN) using deep reinforcement learning (DRL) algorithms. It explores the potential of DRL for learning complex decision-making policies without human intervention. The paper first introduces the C-RAN architecture and resource allocation concepts, followed by an overview of DRL algorithms applied to C-RAN. It discusses the challenges and potential solutions in applying DRL to C-RAN resource allocation, including scalability, convergence, and fairness. The review concludes by highlighting open research directions for future investigation. By providing insights into the state-of-the-art techniques for resource allocation in 5G C-RAN using DRL, this paper emphasizes their potential impact on advancing 5G network technology.
Açıklama
Anahtar Kelimeler
Joint Optimization, C-Ran, Computational Resources, Radio, Networks, Machine, Access, Noma, Framework, Security
Kaynak
Transactions on Emerging Telecommunications Technologies
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
35
Sayı
1
