Double DQN Reinforcement Learning-Based Computational Offloading and Resource Allocation for MEC
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-55471-1_18
Reference20 articles.
1. Liu, J., Zhang, Q.: Offloading schemes in mobile edge computing for ultra-reliable low latency communications. IEEE Access 6, 12825–12837 (2018). https://doi.org/10.1109/ACCESS.2018.2800032
2. Yang, J., Shah, A.A., Pezaros, D.: A survey of energy optimization approaches for computational task offloading and resource allocation in MEC networks. Electronics 12(17), 3548 (2023). https://doi.org/10.3390/electronics12173548
3. Landers, M., Doryab, A.: Deep reinforcement learning verification: a survey. ACM Comput. Surv. 55(14s), Article 330, 31 (2023). https://doi.org/10.1145/3596444
4. Kumaran, K., Sasikala, E.: Learning based latency minimization techniques in mobile edge computing (MEC) systems: a comprehensive survey. In: 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), Puducherry, India, pp. 1–6 (2021). https://doi.org/10.1109/ICSCAN53069.2021.9526410
5. Liu, C.-F., Bennis, M., Poor, H.V.: Latency and reliability-aware task offloading and resource allocation for mobile edge computing. In: 2017 IEEE Globe com Workshops (GC Wkshps), Singapore, pp. 1–7 (2017). https://doi.org/10.1109/GLOCOMW.2017.8269175
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