TY - JOUR
T1 - Channel Access Optimization in Unlicensed Spectrum for Downlink URLLC
T2 - Centralized and Federated DRL Approaches
AU - Liu, Yan
AU - Zhou, Hui
AU - Deng, Yansha
AU - Nallanathan, Arumugam
N1 - Funding Information:
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.
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - 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.
AB - 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.
KW - channel access
KW - deep reinforcement learning
KW - NR-U
KW - URLLC
KW - WiFi
UR - http://www.scopus.com/inward/record.url?scp=85161033605&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2023.3280982
DO - 10.1109/JSAC.2023.3280982
M3 - Article
AN - SCOPUS:85161033605
SN - 0733-8716
VL - 41
SP - 2208
EP - 2222
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 7
ER -