Related papers: Deep Learning for Hybrid 5G Services in Mobile Edg…
Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning applications, and have been widely used on mobile devices. Running DNNs on resource-constrained mobile devices often requires the help from edge servers…
The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through…
Modern power grids are transitioning towards power electronics-dominated grids (PEDG) due to the increasing integration of renewable energy sources and energy storage systems. This shift introduces complexities in grid operation and…
Direct air-to-ground (A2G) communications leveraging the fifth-generation (5G) new radio (NR) can provide high-speed broadband in-flight connectivity to aircraft in the sky. A2G network deployment entails optimizing various design…
This paper presents an enhanced electric vehicle demand response system based on large language models, aimed at optimizing the application of vehicle-to-grid technology. By leveraging an large language models-driven multi-agent framework…
Digital twins (DT) have received significant attention due to their numerous benefits, such as real-time data analytics and cost reduction in production. DT serves as a fundamental component of many applications, encompassing smart…
Recently, the concept of digital twins (DTs) has received significant attention within the realm of 5G/6G. This demonstration shows an innovative DT design and implementation framework tailored toward integration within the 5G…
Electricity load forecasting plays an important role in the energy planning such as generation and distribution. However, the nonlinearity and dynamic uncertainties in the smart grid environment are the main obstacles in forecasting…
Accommodating digital twins (DTs) in the metaverse is essential to achieving digital reality. This need for integrating DTs into the metaverse while operating them at the network edge has increased the demand for a decentralized…
With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network…
Digital transformation in the built environment generates vast data for developing data-driven models to optimize building operations. This study presents an integrated solution utilizing edge computing, digital twins, and deep learning to…
Task offloading is a widely used technology in Mobile Edge Computing (MEC), which declines the completion time of user task with the help of resourceful edge servers. Existing works mainly focus on the case that the computation density of a…
This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning model based on their locally distributed data samples. During the distributed training, we…
Edge intelligent applications like VR/AR and language model based chatbots have become widespread with the rapid expansion of IoT and mobile devices. However, constrained edge devices often cannot serve the increasingly large and complex…
Mobile Edge Computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge, thus acting as an enabler for edge intelligence services empowering massive mobile and Internet of Things…
In future cell-free (or cell-less) wireless networks, a large number of devices in a geographical area will be served simultaneously in non-orthogonal multiple access scenarios by a large number of distributed access points (APs), which…
Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promising approach. It allows the power of DNNs to be leveraged for…
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision, natural language processing, reinforcement learning, etc. The high-performed DNNs heavily rely on intensive resource consumption. For…
The advent of fifth-generation (5G) and Beyond 5G (B5G) networks introduces diverse service requirements, from ultra-low latency to high bandwidth, demanding dynamic monitoring and advanced solutions to ensure Quality of Service (QoS). The…
Digital Twin (DT) is a promising technology for the new immersive digital life with a variety of applications in areas such as Industry 4.0, aviation, and healthcare. Proliferation of this technology requires higher data rates, reliability,…