46 citations · 95 across the 16 of their papers we have counts for
5 papers · 1 filter
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks
Wen Wu, Kaige Qu, Peng Yang +4
In this paper, we study a network slicing problem for edge-cloud orchestrated vehicular networks, in which the edge and cloud servers are orchestrated to process computation tasks…
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning
Wen Wu, Peng Yang, Weiting Zhang +3
Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which r…
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming
Xinyu Huang, Mushu Li, Wen Wu +2
In this paper, we propose a digital twin (DT)-assisted cloud-edge collaborative transcoding scheme to enhance user satisfaction in live streaming. We first present a DT-assisted tr…
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme
Xinyu Huang, Conghao Zhou, Wen Wu +4
In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and…
Split Learning over Wireless Networks: Parallel Design and Resource Management
Wen Wu, Mushu Li, Kaige Qu +6
Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into…