Reinforcement Learning for Radio Resource Management in O-RAN

被引：0

作者：

Galdino, Caio P. ^{[1
]}

Couto, Rodrigo S. ^{[1
]}

de Medeiros, Dianne S. V. ^{[2
]}

Moraes, Igor M. ^{[2
]}

Mattos, Diogo M. F. ^{[2
]}

机构：

[1] Univ Fed Rio Janeiro, PEE, COPPE, GTA, Rio De Janeiro, RJ, Brazil

[2] Univ Fed Fluminense, PPGEET, PGC, MidiaCom, Niteroi, RJ, Brazil

来源：

2024 IEEE 13TH INTERNATIONAL CONFERENCE ON CLOUD NETWORKING, CLOUDNET 2024 | 2024年

基金：

巴西圣保罗研究基金会;

关键词：

Open RAN; Reinforcement Learning; Radio Resource Allocation; 5G Network Slicing; INTELLIGENCE;

D O I：

10.1109/CLOUDNET62863.2024.10815702

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Open RAN is a groundbreaking approach that trends to revolutionize mobile network communication. Open RAN steers in a new era of data-driven decision-making RAN management by introducing intelligent controllers that adapt the Radio Access Network (RAN) in near-real time. As a consequence of the current surge in network traffic, the role of Artificial Intelligence in optimizing the RAN becomes paramount. The fifth-generation mobile networks (5G) further enhance this potential by enabling the allocation of clients through network slices for services such as eMBB, URLLC, and mMTC services. This paper presents a radio resource allocation policy that leverages Reinforcement Learning to make intelligent decisions that align with the objectives of each service in the network. The proposed innovative approach reduces user allocation waiting time by 20% compared to the traditional Round-Robin (RR) policy.

引用

页数：9

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