LEO satellite computation offloading and resource allocation algorithm based on multiagent reinforcement learning

Shuai Wang; Xiye Guo; Changgeng Li; Xiaotian Ma; Xuan Li; Shuai Wang; Jiayang Liu; Xiaohe Han

doi:10.1117/12.3034960

11 July 2024 LEO satellite computation offloading and resource allocation algorithm based on multiagent reinforcement learning

Shuai Wang, Xiye Guo, Changgeng Li, Xiaotian Ma, Xuan Li, Shuai Wang, Jiayang Liu, Xiaohe Han

Author Affiliations +

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321010 (2024) https://doi.org/10.1117/12.3034960
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

An important direction for future 6G mobile communication networks is achieving global wireless coverage. To realize comprehensive access services, research has been conducted on multi-satellite cooperative computation offloading and resource allocation issues based on inter-satellite links (ISL). Ground users adopt a partial offloading scheme, aiming to minimize the weighted sum of ground user latency and system energy consumption, and an optimization problem is established. A joint optimization algorithm for low earth orbit (LEO) satellite task offloading and resource allocation based on the Multi-Agent Proximal Policy Optimization (MAPPO) framework is proposed. For the joint computing issue among LEO satellites, a Kuhn-Munkres algorithm (KM) based on a greedy algorithm for pre-allocation results followed by a secondary allocation has been designed. On the other hand, to accelerate the convergence speed of the reinforcement learning algorithm, an action masking mechanism is introduced. The action space of agents was optimized using the Particle Swarm Optimization algorithm (PSO), obtaining a prior distribution of the action space, thereby narrowing the search space of the algorithm. Simulation results indicate that, compared to the MAPPO method without the use of trajectory rehearsal action masks, the proposed method demonstrates approximately a 51.5% improvement in convergence performance and a 6.4% enhancement in convergence outcomes. Additionally, the average computational delay for tasks remains stable at around 0.32 seconds, which represents a reduction of up to 5.4% compared to PSO.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuai Wang, Xiye Guo, Changgeng Li, Xiaotian Ma, Xuan Li, Shuai Wang, Jiayang Liu, and Xiaohe Han "LEO satellite computation offloading and resource allocation algorithm based on multiagent reinforcement learning", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321010 (11 July 2024); https://doi.org/10.1117/12.3034960

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KEYWORDS

Satellites

Particle swarm optimization

Satellite communications

Data transmission

Mathematical optimization

Telecommunications

Unmanned aerial vehicles

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