TY - JOUR
T1 - Deep reinforcement learning-based joint task and energy offloading in UAV-aided 6G intelligent edge networks
AU - Cheng, Zhipeng
AU - Liwang, Minghui
AU - Chen, Ning
AU - Huang, Lianfen
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - Edge networks are expected to play an important role in 6G where machine learning-based methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile, Unmanned Aerial Vehicle (UAV)-enabled aerial network is significant in 6G networks to achieve seamless coverage and super-connectivity. To this end, a joint task and energy offloading problem is studied under a UAV-aided and energy-constrained intelligent edge network, consisting of a high altitude platform (HAP), multiple UAVs, and on-ground fog computing nodes (FCNs). To guarantee the energy supply of UAVs and FCNs, both simultaneous wireless information and power transfer (SWIPT), as well as laser charging techniques are considered. Specifically, we investigate a scenario where each UAV needs to execute a computation-intensive task during each time slot and can be powered by the laser beam transmitted from the HAP. Due to the limited computation resources, each UAV can offload part of the task and energy to the FCNs for collaborative computing, to reduce local energy consumption and the overall task execution delay by adopting SWIPT. Considering the dynamics of the network, e.g., the time-varying locations of UAVs and available computation resources of FCNs, the problem is formulated as a cooperative multi-agent Markov game for UAVs, which aims to maximize the total system utility, by optimizing the task partitioning and power allocation strategies of each UAV, regarding task size, average delay and energy consumption of task execution. To tackle this problem, we propose a multi-agent soft actor–critic (MASAC)-based approach to resolve the problem. Numerical simulation results prove the superiority of our proposed approach as compared with benchmark methods.
AB - Edge networks are expected to play an important role in 6G where machine learning-based methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile, Unmanned Aerial Vehicle (UAV)-enabled aerial network is significant in 6G networks to achieve seamless coverage and super-connectivity. To this end, a joint task and energy offloading problem is studied under a UAV-aided and energy-constrained intelligent edge network, consisting of a high altitude platform (HAP), multiple UAVs, and on-ground fog computing nodes (FCNs). To guarantee the energy supply of UAVs and FCNs, both simultaneous wireless information and power transfer (SWIPT), as well as laser charging techniques are considered. Specifically, we investigate a scenario where each UAV needs to execute a computation-intensive task during each time slot and can be powered by the laser beam transmitted from the HAP. Due to the limited computation resources, each UAV can offload part of the task and energy to the FCNs for collaborative computing, to reduce local energy consumption and the overall task execution delay by adopting SWIPT. Considering the dynamics of the network, e.g., the time-varying locations of UAVs and available computation resources of FCNs, the problem is formulated as a cooperative multi-agent Markov game for UAVs, which aims to maximize the total system utility, by optimizing the task partitioning and power allocation strategies of each UAV, regarding task size, average delay and energy consumption of task execution. To tackle this problem, we propose a multi-agent soft actor–critic (MASAC)-based approach to resolve the problem. Numerical simulation results prove the superiority of our proposed approach as compared with benchmark methods.
KW - Laser
KW - MASAC
KW - Multi-agent Markov game
KW - SWIPT
KW - Task offloading
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85132712026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132712026&partnerID=8YFLogxK
U2 - 10.1016/j.comcom.2022.06.017
DO - 10.1016/j.comcom.2022.06.017
M3 - Article
AN - SCOPUS:85132712026
SN - 0140-3664
VL - 192
SP - 234
EP - 244
JO - Computer Communications
JF - Computer Communications
ER -