Multi-Agent Deep Reinforcement Learning-Based Computation Offloading in LEO Satellite Edge Computing System | IEEE Journals & Magazine | IEEE Xplore

Multi-Agent Deep Reinforcement Learning-Based Computation Offloading in LEO Satellite Edge Computing System


Abstract:

Efficient computation offloading is crucial for resource-constrained users in low earth orbit (LEO) satellite edge computing system. The proposed Dueling Double Deep Q Ne...Show More

Abstract:

Efficient computation offloading is crucial for resource-constrained users in low earth orbit (LEO) satellite edge computing system. The proposed Dueling Double Deep Q Network (D3QN)-based computation offloading algorithm considers LEO satellite mobility, dynamic load levels, queuing theory, and jointly optimizes system delay and energy consumption. Simulation results show that the proposed algorithm has better system cost than other comparison algorithms.
Published in: IEEE Communications Letters ( Volume: 28, Issue: 10, October 2024)
Page(s): 2352 - 2356
Date of Publication: 08 August 2024

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