Abstract
Thanks to the rapid development of wireless communication technology, i.e., B5G, 6G, mobile edge computing (MEC) has emerged as a promising paradigm to facilitate various mobile applications, such as intelligent connected vehicles, internet of remote things (IoRT), etc. However, IoRT deployed in remote areas, e.g., oceans and deserts where terrestrial communication infrastructures are scarce or even unavailable, still suffers from poor quality of service due to unreliable connectivity. Facing this issue, this paper proposes a space-air-ground integrated MEC framework with heterogeneous space, air, and ground communication resources to provide seamless and high-throughput traffic offloading for IoRT. For the intractable traffic offloading problem of task-intensive IoRT devices in dynamic SAGIN environments due to high-speed satellite movement, we propose a joint optimization method for system bandwidth and transmitting power to minimize total traffic offloading delay in SAGIN. In view of the sequential decision-making property of the problem, we further transform it into a Markov decision problem, which is solved using the popular soft actor-critic reinforcement learning algorithm (SAC) with carefully designed reward functions. Extensive numerical results show that the RL-based traffic offloading policy can substantially reduce the delay of IoRT tasks, compared to baseline traffic offloading methods.
This work was supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY22F020006, and Beijing Municipal Science and Technology Program under Grant No. Z221100007722001.
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Qiu, Y. et al. (2024). Joint Optimization of System Bandwidth and Transmitting Power in Space-Air-Ground Integrated Mobile Edge Computing. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14492. Springer, Singapore. https://doi.org/10.1007/978-981-97-0811-6_8
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