Multi-Agent Deep Reinforcement Learning Based Dynamic Task Offloading in a Device-to-Device Mobile-Edge Computing Network to Minimize Average Task Delay with Deadline Constraints. [PDF]
Sensors (Basel)He H, Yang X, Mi X, Shen H, Liao X.
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A cognitive internet of things resource allocation method based on multi-agent reinforcement learning algorithm. [PDF]
Sci RepWang R, Shen Y, Wang D, Li W.
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Health Workers' Perspectives on Mobile Health Care Learning Stickiness: Mixed Methods Study. [PDF]
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Exploring ISAC: Information-Theoretic Insights. [PDF]
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A novel feature-oriented quality of anything (QoX) framework for end-to-end robotic services in 6G networks. [PDF]
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Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments. [PDF]
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Understanding Mobile OTT Service Users' Resistance to Participation in Wireless D2D Caching Networks. [PDF]
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A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting. [PDF]
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Modeling DRX for D2D Communication
IEEE Internet of Things Journal, 2021Discontinuous reception (DRX) has been included in 4G-LTE as the main power saving mechanism for user equipment (UE). However, the existing 3-state DRX model is not sufficient for new use cases introduced by 4G and 5G. For example, the device discovery process in device to device (D2D) communication has a significant impact on delay and power ...
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