Deep reinforcement learning based computation offloading and resource allocation for MEC

By Ji Li, Hui Gao, Tiejun Lv, Yueming Lu. IEEE Wireless Communications and Networking Conference, 2018

461 citations recorded by the source corpus.

How researchers see this paper

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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “Under the circumstances, a preclassification step can be performed before learning [253] to”
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, methodology): “We can also see that, the proposed DRLO scheme outperforms the DRL scheme in [29].”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, methodology): “As summarized in Table 8, DRL algorithms have been applied in wireless networks for mobile networking [247]–[249], scheduler design [250]–[254], resource allocation [255], [256], [258], [260]–[262], and routing [263], [264].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[393] shed light on resource allocation in a multi-user”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “used for each mobile user to learn the optimal offloading policy from past experiences such as the approaches proposed in [101] and [102].”