Search Results for author:
Found 19 papers, 2 papers with code
Timeliness-Fidelity Tradeoff in 3D Scene Representations
We test our framework and the proposed approach with different well-known 3D scene representation methods.
Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype
Specifically, a graph neural network that is scalable to the number of access points (APs) and mobile devices (MDs) is used for obtaining coarse locations of MDs.
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach
Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs).
Intelligent Mode-switching Framework for Teleoperation
User intention recognition is done at the operator side.
Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation
To enable the generalization of the GNN, we develop a hybrid-task meta-learning (HML) algorithm that trains the initial parameters of the GNN with different communication scenarios during meta-training.
Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications
This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper.
Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks
Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements.
Task-Oriented Prediction and Communication Co-Design for Haptic Communications
In this paper, we propose a task-oriented prediction and communication co-design framework, where the reliability of the system depends on prediction errors and packet losses in communications.
A Scalable Graph Neural Network Decoder for Short Block Codes
In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN).
Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse
This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on tracking the Mean Squared Error (MSE) between a real-world device and its digital model in the metaverse.
Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air
In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links.
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks.
Machine Learning for Massive Industrial Internet of Things
With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements.
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing 30% ~ 50% packet losses).
A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints
Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems.
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in the 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions.
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin
We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server.
