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Found 11 papers, 1 papers with code
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.
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices
Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices.
Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems.
Knowledge-Driven Resource Allocation for D2D Networks: A WMMSE Unrolled Graph Neural Network Approach
This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture.
Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach
However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs.
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.
Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework
Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.
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.
Federated Learning in Multi-RIS Aided Systems
This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs).
Information Theory Signal Processing Information Theory
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.
Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks
Therefore, a framework combining with a deep clustering (DeepCluster) module is developed for STTP at largescale networks in this paper.
