Related papers: Machine Learning at the Edge: A Data-Driven Archit…

Future machine learning (ML) powered applications, such as autonomous driving and augmented reality, involve training and inference tasks with timeliness requirements and are communication and computation intensive, which demands for the…

The ability to perform computation on devices, such as smartphones, cars, or other nodes present at the Internet of Things leads to constraints regarding bandwidth, storage, and energy, as most of these devices are mobile and operate on…

The automotive and telco industries have taken an investment bet on the connected car market, pushing for the digital transformation of the sector by exploiting recent Information and Communication Technology (ICT) progress. As ICT…

In this work, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine…

Edge computing has revolutionized the world of mobile and wireless networks world thanks to its flexible, secure, and performing characteristics. Lately, we have witnessed the increasing use of it to make more performing the deployment of…

The widespread deployment of 5G networks, together with the coexistence of 4G/LTE networks, provides mobile devices a diverse set of candidate cells to connect to. However, associating mobile devices to cells to maximize overall network…

Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several…

Machine Learning (ML) is a common tool to interpret and predict the behavior of distributed computing systems, e.g., to optimize the task distribution between devices. As more and more data is created by Internet of Things (IoT) devices,…

Accurate predictions of base stations' traffic load are essential to mobile cellular operators and their users as they support the efficient use of network resources and allow delivery of services that sustain smart cities and roads.…

The need for efficient use of network resources is continuously increasing with the grow of traffic demand, however, current mobile systems have been planned and deployed so far with the mere aim of enhancing radio coverage and capacity.…

Edge computing is considered a key paradigm for supporting real-time applications over 5G networks, as hosting applications at the network edge can substantially reduce delays. A significant fraction of real-time applications over 5G are…

Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement…

Network slicing is one of the most critical 5G pillars. It allows for sharing a 5G infrastructure among different tenants leading to improved service customisation and increased operators' revenues. Concurrently, introducing the…

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant…

Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising…

The emerging technology of multi-tenancy network slicing is considered as an essential feature of 5G cellular networks. It provides network slices as a new type of public cloud services, and therewith increases the service flexibility and…

Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key…

The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance,…

The Fifth Generation (5G) mobile core network is designed as a set of Virtual Network Functions (VNFs) hosted on Commercial-Off-the-Shelf (COTS) hardware. This creates a growing demand for general-purpose compute resources as 5G deployments…

Recently, the development of mobile edge computing has enabled exhilarating edge artificial intelligence (AI) with fast response and low communication cost. The location information of edge devices is essential to support the edge AI in…