Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC: Centralized and Federated DRL Approaches

Yan Liu; Hui Zhou; Yansha Deng; Arumugam Nallanathan

doi:10.1109/JSAC.2023.3280982

Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC: Centralized and Federated DRL Approaches

Yan Liu, Hui Zhou, Yansha Deng, Arumugam Nallanathan

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.

Original language	English
Pages (from-to)	2208-2222
Number of pages	15
Journal	IEEE Journal on Selected Areas in Communications
Volume	41
Issue number	7
Early online date	29 May 2023
DOIs	https://doi.org/10.1109/JSAC.2023.3280982
Publication status	Published - 1 Jul 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 1983-2012 IEEE.

Funding

This work was supported in part by the Shanghai Sailing Program under Grant 23YF1449400; in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/W004348/1; in part by the U.K. Research and Innovation (UKRI) under the U.K. Government s Horizon Europe Funding Guarantee under Grant 10061781; in part by the European Commission-Funded Collaborative Project VERGE Under Smart Networks and Services Joint Undertaking (SNS JU) Program under Grant 101096034; and in part by the Fundamental Research Funds for the Central Universities under Grant 22120230262.

Funders	Funder number
Horizon Europe	10061781, 101096034
Shanghai Sailing Program	23YF1449400
UK Research and Innovation
Engineering and Physical Sciences Research Council	EP/W004348/1
Engineering and Physical Sciences Research Council
Fundamental Research Funds for the Central Universities	22120230262
Fundamental Research Funds for the Central Universities

Keywords

NR-U
URLLC
WiFi
channel access
deep reinforcement learning

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering

Access to Document

10.1109/JSAC.2023.3280982

Cite this

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title = "Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC: Centralized and Federated DRL Approaches",

abstract = "The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.",

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Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC: Centralized and Federated DRL Approaches

Abstract

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Keywords

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