TY - CHAP
T1 - An adaptive full-duplex deep reinforcement learning-based design for 5G-V2X mode 4 VANETs
AU - Zang, Junwei
AU - Shikh-Bahaei, Mohammad
N1 - Funding Information:
VI. ACKNOWLEDGEMENT This work is partially supported by EPSRC under grant number EP/P003486/1.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper exploits full-duplex (FD) technology and deep reinforcement learning (DRL) algorithm jointly and adaptively to enhance the performance of 5G-V2X networks that operate based on the 5G-V2X Mode 4 standard. Specifically, we propose a novel physical- (PHY) and medium access control- (MAC) layer cross-layer design, in which collision detection capability is enabled during broadcasting without introducing redundant signalling. Besides, the resource reservation scheme, collision resolution mechanism and scheduling policy are also designed. As the proposed adaptive method is fully decentralised, vehicular users adapt to the unknown and fast-changing environment autonomously without any help from gNBs. Simulation results demonstrate the superiority of our proposed design over the standardised sensing-based semi-persistent scheduling (SB-SPS) protocol. Therefore, the proposed cross-layer design can be considered as a solution for future 5G-V2X VANETs.
AB - This paper exploits full-duplex (FD) technology and deep reinforcement learning (DRL) algorithm jointly and adaptively to enhance the performance of 5G-V2X networks that operate based on the 5G-V2X Mode 4 standard. Specifically, we propose a novel physical- (PHY) and medium access control- (MAC) layer cross-layer design, in which collision detection capability is enabled during broadcasting without introducing redundant signalling. Besides, the resource reservation scheme, collision resolution mechanism and scheduling policy are also designed. As the proposed adaptive method is fully decentralised, vehicular users adapt to the unknown and fast-changing environment autonomously without any help from gNBs. Simulation results demonstrate the superiority of our proposed design over the standardised sensing-based semi-persistent scheduling (SB-SPS) protocol. Therefore, the proposed cross-layer design can be considered as a solution for future 5G-V2X VANETs.
KW - 5G-V2X Mode 4 standard
KW - Decentralised network
KW - Deep reinforcement learning
KW - Full duplex
KW - VANETs
UR - http://www.scopus.com/inward/record.url?scp=85119345418&partnerID=8YFLogxK
U2 - 10.1109/WCNC49053.2021.9417550
DO - 10.1109/WCNC49053.2021.9417550
M3 - Conference paper
AN - SCOPUS:85119345418
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
Y2 - 29 March 2021 through 1 April 2021
ER -