1.4k citations · 1.5k across the 17 of their papers we have counts for
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Deep Reinforcement Learning for IoT Networks: Age of Information and Energy Cost Tradeoff
Xiongwei Wu, Xiuhua Li, Jun Li +2
In most Internet of Things (IoT) networks, edge nodes are commonly used as to relays to cache sensing data generated by IoT sensors as well as provide communication services for da…
Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic
Xiongwei Wu, Xiuhua Li, Jun Li +3
Edge nodes (ENs) in Internet of Things commonly serve as gateways to cache sensing data while providing accessing services for data consumers. This paper considers multiple ENs tha…
Multi-Agent Reinforcement Learning for Cooperative Coded Caching via Homotopy Optimization
Xiongwei Wu, Jun Li, Ming Xiao +2
Introducing cooperative coded caching into small cell networks is a promising approach to reducing traffic loads. By encoding content via maximum distance separable (MDS) codes, co…
Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning
Q. Liu, L. Shi, L. Sun +3
In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing network, where the UAV executes computational tasks offloaded from mobile terminal users (TU…
Dynamic Content Update for Wireless Edge Caching via Deep Reinforcement Learning
Pingyang Wu, Jun Li, Long Shi +3
This letter studies a basic wireless caching network where a source server is connected to a cache-enabled base station (BS) that serves multiple requesting users. A critical probl…