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Learning-Based Query Scheduling and Resource Allocation for Low-Latency Mobile Edge Video Analytics
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Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions
5G and beyond is expected to enable various emerging use cases with diverse performance requirements from vertical industries. To serve …
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Towards Secure and Intelligent Network Slicing for 5G Networks
Network slicing is one of the emerging technologies allowing resource sharing among different network entities in 5G networks. It …
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FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction
Predicting traffic flow plays an important role in reducing traffic congestion and improving transportation efficiency for smart …
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Towards Revenue-Driven Multi-User Online Task Offloading in Edge Computing
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A survey on sleep mode techniques for ultra-dense networks in 5G and beyond
The proliferation of mobile users with an attendant rise in energy consumption mainly at the base station has requested new ways of …
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MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
Most existing deep neural networks are static, which means they can only do inference at a fixed complexity. But the resource budget …
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Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems
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FedVision: Federated Video Analytics With Edge Computing
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Finite Control Set Model Predictive Control with Secondary Problem Formulation for Power Loss and Thermal Stress Reductions
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