Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning

Peizheng Li, Hakan Erdol, Keith Briggs, Xiaoyang Wang, Robert J Piechocki, Abdelrahim Ahmad, Rui Inacio, Shipra Kapoor, Angela Doufexi, Arjun Parekh

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Setting the transmit power setting of 5G cells has been a long-term topic of discussion, as optimized power settings can help reduce interference and improve the quality of service to users. Recently, machine learning (ML)-based, especially reinforcement learning (RL)-based control methods have received much attention. However, there is little discussion about the generalisation ability of the trained RL models. This paper points out that an RL agent trained in a specific indoor environment is room-dependent, and cannot directly serve new heterogeneous environments. Therefore, in the context of Open Radio Access Network (O-RAN), this paper proposes a distributed cell power-control scheme based on Federated Reinforcement Learning (FRL). Models in different indoor environments are aggregated to the global model during the training process, and then the central server broadcasts the updated model back to each client. The model will also be used as the base model for adaptive training in the new environment. The simulation results show that the FRL model has similar performance to a single RL agent, and both are better than the random power allocation method and exhaustive search method. The results of the generalisation test show that using the FRL model as the base model improves the convergence speed of the model in the new environment.
Original languageEnglish
Title of host publication2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)978-1-6654-5468-1
ISBN (Print)978-1-6654-5469-8
Publication statusPublished - 18 Jan 2023
Event2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) -
Duration: 26 Sept 202229 Sept 2022

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

Bibliographical note

Funding Information:
This work was developed within the Innovate UK/CELTIC-NEXT European collaborative project on AIMM (AI-enabled Massive MIMO). This work has also been funded in part by the Next-Generation Converged Digital Infrastructure (NG-CDI) Project, supported by BT and Engineering and Physical Sciences Research Council (EPSRC), Grant ref. EP/R004935/1.

Publisher Copyright:
© 2022 IEEE.


  • cs.NI
  • cs.LG


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