Related papers: Network Digital Twin for Congestion-Aware Predicti…
Although the emergence of 6G IoT networks has accelerated the deployment of enhanced smart city services, the resource limitations of IoT devices remain as a significant problem. Given this limitation, meeting the low-latency service…
As urban environments grow, the modelling of transportation systems becomes increasingly complex. This paper advances the field of travel demand modelling by introducing advanced Graph Neural Network (GNN) architectures as surrogate models,…
Intelligent Transportation System (ITS) is crucial for improving traffic congestion, reducing accidents, optimizing urban planning, and more. However, the complexity of traffic networks has rendered traditional machine learning and…
Digital twin (DT) technology is increasingly used in urban planning, leveraging real-time data integration for environmental monitoring. This paper presents an urban-focused DT that combines computational fluid dynamics simulations with…
Digital twin (DT) is revolutionizing the emerging video streaming services through tailored network management. By integrating diverse advanced communication technologies, DTs are promised to construct a holistic virtualized network for…
Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular…
Digital twin (DT) technology can replicate physical entities in cyberspace. A mobility DT digitalizes connected and autonomous vehicles (CAVs) and their surrounding traffic environment, allowing to monitor the maneuvering and distribution…
To capture spatial relationships and temporal dynamics in traffic data, spatio-temporal models for traffic forecasting have drawn significant attention in recent years. Most of the recent works employed graph neural networks(GNN) with…
We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance,…
Digital twin (DT) technology enables real-time simulation, prediction, and optimization of physical systems, but practical deployment faces challenges from high data requirements, proprietary data constraints, and limited adaptability to…
In this paper, we design a resource management scheme to support stateful applications, which will be prevalent in 6G networks. Different from stateless applications, stateful applications require context data while executing computing…
In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be…
The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (DTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the DT…
Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities and data centers. With demands growing at exponential rates,…
Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks…
Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world applications. Current studies use existing techniques, such…
In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network. Specifically, the users perform radar sensing and computation offloading on the same spectrum, while unmanned aerial vehicles…
All the existing real world networks are evolving, hence, study of traffic dynamics in these enlarged networks is a challenging task. The critical issue is to optimize the network structure to improve network capacity and avoid traffic…
Recent studies on transportation networks have shown that real-time route guidance can inadvertently induce congestion or oscillatory traffic patterns. Nevertheless, such technologies also offer a promising opportunity to manage traffic…
This study follows many classical approaches to multi-object tracking (MOT) that model the problem using dynamic graphical data structures, and adapts this formulation to make it amenable to modern neural networks. Our main contributions in…