activity
20192021
most citedDeep Reinforcement Learning for IoT Networks: Age of Information and Energy Cost Tradeoff

4 citations · 15 across the 7 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.CR2020★ 2 cited

Blockchain-Based Approach for Securing Spectrum Trading in Multibeam Satellite Systems

Feng Li, Kwok-Yan Lam, Min Jia +3

This paper presents a blockchain-based approach for securing spectrum sharing in multi-beam satellite systems. Satellite spectrum is a scarce resource that requires highly efficien…

cs.IT2020★ 4 cited

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…

cs.IT2020★ 4 cited

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…

cs.NI2020★ 1 cited

PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks

Qilin Fan, Xiuhua Li, Jian Li +3

As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content…

eess.SP2020★ 3 cited

Joint Long-Term Cache Updating and Short-Term Content Delivery in Cloud-Based Small Cell Networks

Xiongwei Wu, Qiang Li, Xiuhua Li +2

Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users' quality of servic…