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Found 10 papers, 0 papers with code
Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication
A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas.
User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality
The proposed approach aims to maximize VR video streaming performance, i. e., minimizing video frame missing rate, by proactively caching popular VR video chunks and adaptively scheduling computing resources at an edge server based on user and network dynamics.
Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality
In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server.
Holistic Network Virtualization and Pervasive Network Intelligence for 6G
The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively.
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming
Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively.
Digital Twin-Empowered Network Planning for Multi-Tier Computing
Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from reconfiguring resource reservation.
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme
In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE).
AI-Native Network Slicing for 6G Networks
Then, network slicing solutions are studied to support emerging AI services by constructing AI instances and performing efficient resource management, i. e., slicing for AI.
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning
To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost.
Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks
Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications.
