A collaborative optimization strategy for computing offloading and resource allocation based on multi-agent deep reinforcement learning

• Published in: Computers & electrical engineering Vol. 103; p. 108278
• Main Authors: Jiang, Yingying, Mao, Yuxuan, Wu, Gaoxiang, Cai, Zhenhua, Hao, Yixue
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022
• Subjects: MADRL; MEC; Resource allocation; Task offloading; MADRL; MEC; Task offloading; Resource allocation
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2022.108278

With the emergence of mobile edge computing (MEC), the edge cloud with certain computing power is deployed closer to the mobile device, which can well solve the computing and delay requirements of the mobile device. In 5G ultra-dense heterogeneous networks, where the macro base station (MBS) and multiple dense small base stations (SBS) are deployed in the region, the offloading decision faces multiple choices. In order to solve the problem of computing offloading and resource allocation in 5G ultra-dense heterogeneous networks, we propose a collaborative optimization strategy based on multi-agent deep reinforcement learning (MADRL). At each time, the mobile device only needs to make the optimal offloading decision according to its own historical offloading decision, the allocated bandwidth and computing resources at the past time, as well as the service response delay and energy consumption at the past time, without knowing other user information and dynamic network environment information. Simulation results show that the proposed collaborative optimization strategy is better than the other three baseline schemes in terms of service response delay and energy consumption performance. [Display omitted] •A joint optimization strategy of task offloading and resource allocation for 5G ultra-dense networks is proposed.•The offloading decision of mobile devices is made based on MAPPO.•The heterogeneous edge clouds realize the allocation of bandwidth and computing resources based on convex optimization.