Reinforcement-Learning Based Radio Resources Allocation in Licensed Assisted Access

Chiapin Wang*, Yu Chia Liu, Han Chi Gao, Tsung Yi Fan Chiang

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

摘要

In the next-generation heterogeneous networks, 5G New Radio (NR) base stations will contend with Wi-Fi access points for the use of unlicensed frequency bands to increase the transmission rate. In 3GPP Licensed Assisted Access (LAA) standards, the technology called Listen Before Talk (LBT) is introduced for the coexistence of NR base stations and Wi-Fi access points. However, the contention for unlicensed bands between LAA and Wi-Fi is rather unfair; throughput with LBT used in LAA is much higher than that with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) used in Wi-Fi. Since the performance of Wi-Fi when coexisting with LAA mainly relies on how the LBT parameters are configured by the LAA, we propose in this paper a radio resources allocation scheme which adjusts the Transmission Opportunity (TXOP) duration of LAA based on reinforcement learning to improve the fairness between LAA and Wi-Fi. The simulation results illustrate that the proposed scheme effectively improves the fairness between LAA and Wi-Fi in terms of throughput.

原文英語
主出版物標題Proceedings - 2020 IEEE International Conference on Smart Cloud, SmartCloud 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面169-174
頁數6
ISBN(電子)9781728165479
DOIs
出版狀態已發佈 - 2020 十一月
事件5th IEEE International Conference on Smart Cloud, SmartCloud 2020 - Washington, 美国
持續時間: 2020 十一月 62020 十一月 8

出版系列

名字Proceedings - 2020 IEEE International Conference on Smart Cloud, SmartCloud 2020

會議

會議5th IEEE International Conference on Smart Cloud, SmartCloud 2020
國家/地區美国
城市Washington
期間2020/11/062020/11/08

ASJC Scopus subject areas

  • 管理資訊系統
  • 人工智慧
  • 電腦網路與通信
  • 資訊系統與管理

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