TY - GEN
T1 - A Novel Multi-Objective Routing Scheme based on Cooperative Multi-Agent Reinforcement Learning for Metaverse Services in Fixed 6G
AU - Zhou, Xueming
AU - Mao, Bomin
AU - Liu, Jiajia
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The 6th Generation Fixed networks (F6G) with holographic communication and omni-directional sensory coverage is expected to arrive in 2030. Due to the characteristics of cross-integration between the physical and digital worlds, metaverse has been widely recognized as an important application in F6G to be utilized in all walks of life in the future. However, the metaverse applications will generate diversified communication services with differentiated Quality of Service (QoS) requirements, which will be a great challenge for F6G to develop End-to-End (E2E) customized transmission strategies. Traditional single metric-based routing algorithms cannot efficiently orchestrate the network resources to meet the diversified QoS requirements. To solve the above problems, we propose a Cooperative Multi-Agent Reinforcement Learning (Co-MARL) routing algorithm, which measures the differentiated QoS demands through a generic utility function to facilitate multiple agents to solve the multi-objective optimization problem. The simulation results show our scheme outperforms the traditional routing algorithm in meeting the diversified QoS requirements.
AB - The 6th Generation Fixed networks (F6G) with holographic communication and omni-directional sensory coverage is expected to arrive in 2030. Due to the characteristics of cross-integration between the physical and digital worlds, metaverse has been widely recognized as an important application in F6G to be utilized in all walks of life in the future. However, the metaverse applications will generate diversified communication services with differentiated Quality of Service (QoS) requirements, which will be a great challenge for F6G to develop End-to-End (E2E) customized transmission strategies. Traditional single metric-based routing algorithms cannot efficiently orchestrate the network resources to meet the diversified QoS requirements. To solve the above problems, we propose a Cooperative Multi-Agent Reinforcement Learning (Co-MARL) routing algorithm, which measures the differentiated QoS demands through a generic utility function to facilitate multiple agents to solve the multi-objective optimization problem. The simulation results show our scheme outperforms the traditional routing algorithm in meeting the diversified QoS requirements.
KW - Cooperative Multi-Agent Reinforcement Learning
KW - F6G
KW - differentiated QoS requirements
KW - multi-objective routing
UR - https://www.scopus.com/pages/publications/85162711454
U2 - 10.1109/WOCC58016.2023.10139544
DO - 10.1109/WOCC58016.2023.10139544
M3 - 会议稿件
AN - SCOPUS:85162711454
T3 - 32nd Wireless and Optical Communications Conference, WOCC 2023
BT - 32nd Wireless and Optical Communications Conference, WOCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd Wireless and Optical Communications Conference, WOCC 2023
Y2 - 5 May 2023 through 6 May 2023
ER -