Meta Reinforcement Learning-Based Computation Offloading in RIS-Aided MEC-Enabled Cell-Free RAN
Lu, Yi; Jiang, Yanxiang; Zhang, Lingling; Bennis, Mehdi; Niyato, Dusit; You, Xiaohu (2023-10-23)
IEEE
23.10.2023
Y. Lu, Y. Jiang, L. Zhang, M. Bennis, D. Niyato and X. You, "Meta Reinforcement Learning-Based Computation Offloading in RIS-Aided MEC-Enabled Cell-Free RAN," ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 5370-5376, doi: 10.1109/ICC45041.2023.10278860
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502061475
https://urn.fi/URN:NBN:fi:oulu-202502061475
Tiivistelmä
Abstract
In this paper, the computation offloading problem in reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC)-enabled cell-free radio access network (CF-RAN) is investigated. To minimize the average task execution delay, we propose to formulate a joint optimization problem of computation offloading and RIS phase shifts. Considering the non-deterministic polynomial hard (NP-hard) property of this problem and time-varying network environment, we further propose a meta reinforcement learning (meta-RL)-based computation offloading policy, which can adapt to new environment quickly with only a few gradient updates. By aggregating powerful decision-making ability of conventional RL and rapid environment learning ability of meta-learning, our proposed policy can find the optimal strategy in very fast speed. Simulation results show that our proposed meta-RL-based computation offloading policy reduces the average task execution delay by 25% compared to the considered two state-of-the-art benchmark policies.
In this paper, the computation offloading problem in reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC)-enabled cell-free radio access network (CF-RAN) is investigated. To minimize the average task execution delay, we propose to formulate a joint optimization problem of computation offloading and RIS phase shifts. Considering the non-deterministic polynomial hard (NP-hard) property of this problem and time-varying network environment, we further propose a meta reinforcement learning (meta-RL)-based computation offloading policy, which can adapt to new environment quickly with only a few gradient updates. By aggregating powerful decision-making ability of conventional RL and rapid environment learning ability of meta-learning, our proposed policy can find the optimal strategy in very fast speed. Simulation results show that our proposed meta-RL-based computation offloading policy reduces the average task execution delay by 25% compared to the considered two state-of-the-art benchmark policies.
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