363 citations · 784 across the 20 of their papers we have counts for
21 papers
Unified Evaluation Methodology for AI-Native Integrated Sensing and Communication
Filip Lemic, Andra Blaga, Francesco Devoti +24
Integrated Sensing and Communication (ISAC) couples radio sensing, data transmission, and control actions within a single closed-loop system. When Artificial Intelligence (AI)-driv…
Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility
Sina Beyraghi, Masoud Sadeghian, Firdous Bin Ismail +3
This paper explores the use of generative models to synthesize high-quality, site-specific multiple-input multiple-output (MIMO) channel data, addressing the high cost of the exten…
From Waves to Graphs: A Ray-Tracing-Inspired Neural Radio Propagation Model
Paul Almasan, Stefanos Bakirtzis, José Suárez-Varela +1
Artificial intelligence-driven radio propagation models provide agile and robust solutions for mobile network operators in their effort to ensure the optimal performance of the wir…
Direct-to-Device Connectivity for Integrated Communication, Navigation and Surveillance
Muhammad Asad Ullah, Davi Brilhante, Luís Eduardo Partichelli Potrich +4
Sixth-generation (6G) communication systems are expected to support direct-to-device (D2D) connectivity, enabling standard user equipment (UE) to seamlessly transition to non-terre…
Data-Driven Deployment of Reconfigurable Intelligent Surfaces in Cellular Networks
Sina Beyraghi, Javad Shabanpour, Giovanni Geraci +2
This paper presents a fully automated, data-driven framework for the large-scale deployment of reconfigurable intelligent surfaces (RISs) in cellular networks. Leveraging physicall…
Site-Specific RIS Deployment in Cellular Networks via Calibrated Ray Tracing
Sina Beyraghi, Javad Shabanpour, Giovanni Geraci +2
This work introduces a fully-automated RIS deployment strategy validated through a digital twin, powered by Sionna ray tracing, of a UK city. On a scene calibrated with measured da…
Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question
Stefanos Bakirtzis, Paul Almasan, José Suárez-Varela +5
Differentiable ray tracing has recently challenged the status quo in radio propagation modelling and digital twinning. Promising unprecedented speed and the ability to learn from r…
Atom: Neural Traffic Compression with Spatio-Temporal Graph Neural Networks
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
Storing network traffic data is key to efficient network management; however, it is becoming more challenging and costly due to the ever-increasing data transmission rates, traffic…
Enhancing 5G Radio Planning with Graph Representations and Deep Learning
Paul Almasan, José Suárez-Varela, Andra Lutu +2
The roll out of new mobile network generations poses hard challenges due to various factors such as cost-benefit tradeoffs, existing infrastructure, and new technology aspects. In…
Leveraging Spatial and Temporal Correlations for Network Traffic Compression
Paul Almasan, Krzysztof Rusek, Shihan Xiao +4
The deployment of modern network applications is increasing the network size and traffic volumes at an unprecedented pace. Storing network-related information (e.g., traffic traces…
Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing
Krzysztof Rusek, Paul Almasan, José Suárez-Varela +3
Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewis…
Network Digital Twin: Context, Enabling Technologies and Opportunities
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail…
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies
Carlos Güemes-Palau, Paul Almasan, Shihan Xiao +4
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a resul…
Digital Twin Network: Opportunities and Challenges
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. Thes…
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés +8
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems).…
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning
Paul Almasan, Shihan Xiao, Xiangle Cheng +3
Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, i…
The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks
José Suárez-Varela, Miquel Ferriol-Galmés, Albert López +21
During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of contro…
Towards Real-Time Routing Optimization with Deep Reinforcement Learning: Open Challenges
Paul Almasan, José Suárez-Varela, Bo Wu +3
The digital transformation is pushing the existing network technologies towards new horizons, enabling new applications (e.g., vehicular networks). As a result, the networking comm…
Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
Paul Almasan, José Suárez-Varela, Krzysztof Rusek +2
Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficien…
RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
Krzysztof Rusek, José Suárez-Varela, Paul Almasan +2
Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to…
Securing the Control-plane Channel and Cache of Pull-based ID/LOC Protocols
Paul Almasan, Jordi Paillisse, Alberto Rodriguez-Natal +5
Pull-based ID/LOC split protocols, such as LISP (RFC6830), retrieve mappings from a mapping system to encapsulate and forward packets. This is done by means of a control-plane chan…