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View article: k-scale: k-Anonymizing Millions of Trajectories
k-scale: k-Anonymizing Millions of Trajectories Open
International audience
Quantum Computing in the RAN with Qu4Fec: Closing Gaps Towards Quantum-based FEC processors Open
In mobile communication systems, the increasing densification of radio access networks is creating unprecedented computational stress for baseband processing, threatening the industry's sustainability, and new computing paradigms are urgen…
Pontus: A Memory-Efficient and High-Accuracy Approach for Persistence-Based Item Lookup in High-Velocity Data Streams Open
In today's web-scale, data-driven environments, real-time detection of persistent items that consistently recur over time is essential for maintaining system integrity, reliability, and security. Persistent items often signal critical anom…
Real‐Time Encrypted Traffic Classification in Programmable Networks with P4 and Machine Learning Open
Network traffic encryption has been on the rise in recent years, making encrypted traffic classification (ETC) an important area of research. Machine learning (ML) methods for ETC are widely regarded as the state of the art. However, most …
Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models Open
The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typically acquired through radio propagation solvers, which ho…
Towards Physics-Informed Graph Neural Network-based Computational Electromagnetics Open
This paper presents a generalizable data-driven computational electromagnetics (CEM) framework leveraging graph neural networks (GNNs). The proposed model supports training and inference for CEM scenarios with different simulation domain s…
CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing Open
Open and virtualized Radio Access Networks (vRANs) are breeding a new market with unprecedented opportunities. However, carrier-grade vRANs today are expensive and energy-hungry, as they rely on hardware accelerators (HAs) that are dedicat…
Investigating Second-Level Digital Divides in Nationwide Mobile Service Consumption Open
The widespread availability of inexpensive mobile broadband has democratized access to digital services in developed countries. While this has supposedly closed digital divides among the population, more subtle inequalities may still be pr…
Jewel: Resource-Efficient Joint Packet and Flow Level Inference in Programmable Switches Open
Embedding machine learning (ML) models in programmable switches realizes the vision of high-throughput and low-latency inference at line rate. Recent works have made breakthroughs in embedding Random Forest (RF) models in switches for eith…
Designing the Network Intelligence Stratum for 6G Networks Open
As network complexity escalates, there is an increasing need for more sophisticated methods to manage and operate these networks, focusing on enhancing efficiency, reliability, and security. A wide range of Artificial Intelligence (AI)/Mac…
Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine Learning Open
Encrypted Traffic Classification (ETC) has become an important area of research with Machine Learning (ML) methods being the state-of-the-art. However, most existing solutions either rely on offline ETC based on collected network data or o…
Towards Data-Driven Management of Mobile Networks through User Plane Inference Open
Growing network complexity has rendered human-in-the-loop network management approaches obsolete. The advent of Software-Defined Networking (SDN) has enabled network automation, with Machine Learning (ML) models running in the control plan…
AI-Assisted Indoor Wireless Network Planning With Data-Driven Propagation Models Open
Propelled by rapid advances in artificial intelligence (AI), the design and operation of 5G and beyond networks are anticipated to be radically different from those of legacy communication systems. Indeed, AI can be exploited to automate a…
View article: ETHER: A 6G Architectural Framework for 3D Multi-Layered Networks
ETHER: A 6G Architectural Framework for 3D Multi-Layered Networks Open
peer reviewed
A Joint Optimization Approach for Power-Efficient Heterogeneous OFDMA Radio Access Networks Open
Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, t…
Explainable and Transferable Loss Meta-Learning for Zero-Touch Anticipatory Network Management Open
Zero-touch network management is one of the most ambitious yet strongly required paradigms for beyond 5G and 6G mobile communication systems. Achieving full automation requires a closed loop that combines (i) network status data collection…
Measurement And Evaluation Of Noise Contribution Of A Major Infrastructure Yard According To UNI 10855:1999 Standard Open
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Characterizing Mobile Service Demands at Indoor Cellular Networks Open
Indoor cellular networks (ICNs) are anticipated to become a principal component of 5G and beyond systems. ICNs aim at extending network coverage and enhancing users' quality of service and experience, consequently producing a substantial v…
View article: Spotting Deep Neural Network Vulnerabilities in Mobile Traffic Forecasting with an Explainable AI Lens
Spotting Deep Neural Network Vulnerabilities in Mobile Traffic Forecasting with an Explainable AI Lens Open
The ability to forecast mobile traffic patterns is key to resource management for mobile network operators and planning for local authorities. Several Deep Neural Networks (DNN) have been designed to capture the complex spatiotemporal char…
kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices Open
Cloud-native mobile networks pave the road for Network Slicing as a Service (NSaaS), where slice overbooking is a promising management strategy to maximize the revenues from admitted slices by exploiting the fact they are unlikely to fully…
Orchestration Procedures for the Network Intelligence Stratum in 6G Networks Open
The quest for autonomous mobile networks introduces the need for fully native support for Network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI strat…
Orchestration Procedures for the Network Intelligence Stratum in 6G Networks Open
The quest for autonomous mobile networks introdu-ces the need for fully native support for Network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI stra…
ETHER: Energy- and cost-efficient framework for seamless connectivity over the integrated terrestrial and non-terrestrial 6G networks Open
Several use cases already proposed for 5G networks cannot be facilitated by terrestrial infrastructure, either due to its small penetration in remote/rural areas or the harsh propagation conditions due to the terrain. Indicative applicatio…
The NetMob23 Dataset: A High-resolution Multi-region Service-level Mobile Data Traffic Cartography Open
Digital sources have been enabling unprecedented data-driven and large-scale investigations across a wide range of domains, including demography, sociology, geography, urbanism, criminology, and engineering. A major barrier to innovation i…