TY - GEN
T1 - Reinforcement-Learning Based Radio Resources Allocation in Licensed Assisted Access
AU - Wang, Chiapin
AU - Liu, Yu Chia
AU - Gao, Han Chi
AU - Chiang, Tsung Yi Fan
N1 - Funding Information:
This study was supported by the Ministry of Science and Technology, Taiwan, under grant number MOST 108-2221-E- 003-003 and MOST 109-2221-E-003-017.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Heterogeneous networks
KW - Licensed Assisted Access (LAA)
KW - Q-Learning
KW - Reinforcement learning
KW - Unlicensed band
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U2 - 10.1109/SmartCloud49737.2020.00040
DO - 10.1109/SmartCloud49737.2020.00040
M3 - Conference contribution
AN - SCOPUS:85098513841
T3 - Proceedings - 2020 IEEE International Conference on Smart Cloud, SmartCloud 2020
SP - 169
EP - 174
BT - Proceedings - 2020 IEEE International Conference on Smart Cloud, SmartCloud 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Smart Cloud, SmartCloud 2020
Y2 - 6 November 2020 through 8 November 2020
ER -