23 citations · 58 across the 52 of their papers we have counts for
15 papers · 1 filter
A Family of Open Time-Series Foundation Models for the Radio Access Network
Ioannis Panitsas, Leandros Tassiulas
The Radio Access Network (RAN) is evolving into a programmable and disaggregated infrastructure that increasingly relies on AI-native algorithms for optimization and closed-loop co…
5GC-Bench: A Framework for Stress-Testing and Benchmarking 5G Core VNFs
Ioannis Panitsas, Tolga O. Atalay, Dragoslav Stojadinovic +2
The disaggregated, cloud-native design of the 5G Core (5GC) enables flexibility and scalability but introduces significant challenges. Control-plane procedures involve complex inte…
SlicePilot: Demystifying Network Slice Placement in Heterogeneous Cloud Infrastructures
Ioannis Panitsas, Tolga O. Atalay, Dragoslav Stojadinovic +2
Cellular networks are comprised of software-based entities, with main functions encapsulated as Virtual Network Functions (VNFs) deployed on Commercial-off-the-Shelf (COTS) hardwar…
Agoran: An Agentic Open Marketplace for 6G RAN Automation
Ilias Chatzistefanidis, Navid Nikaein, Andrea Leone +5
Next-generation mobile networks must reconcile the often-conflicting goals of multiple service owners. However, today's network slice controllers remain rigid, policy-bound, and un…
D2Q Synchronizer: Distributed SDN Synchronization for Time Sensitive Applications
Ioannis Panitsas, Akrit Mudvari, Leandros Tassiulas
In distributed Software-Defined Networking (SDN), distributed SDN controllers require synchronization to maintain a global network state. Despite the availability of synchronizatio…
FedJam: Multimodal Federated Learning Framework for Jamming Detection
Ioannis Panitsas, Iason Ofeidis, Leandros Tassiulas
Jamming attacks pose a critical threat to wireless networks, yet existing detection methods remain largely unimodal, centralized and resource-intensive, limiting their performance,…
JamShield: A Machine Learning Detection System for Over-the-Air Jamming Attacks
Ioannis Panitsas, Yagmur Yigit, Leandros Tassiulas +2
Wireless networks are vulnerable to jamming attacks due to the shared communication medium, which can severely degrade performance and disrupt services. Despite extensive research,…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate
Yifei Jin, Ali Maatouk, Sarunas Girdzijauskas +3
Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional (3D) wireless channel modeling, driven by advances in graphical rendering. Current approaches struggle to…
Throughput-Optimal Scheduling via Rate Learning
Panagiotis Promponas, Víctor Valls, Konstantinos Nikolakakis +2
We study the problem of designing scheduling policies for communication networks. This problem is often addressed with max-weight-type approaches since they are throughput-optimal.…
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach
Ioannis Panitsas, Akrit Mudvari, Ali Maatouk +1
Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands…
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
Ioannis Panitsas, Akrit Mudvari, Leandros Tassiulas
In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a cri…
Joint SDN Synchronization and Controller Placement in Wireless Networks using Deep Reinforcement Learning
Akrit Mudvari, Leandros Tassiulas
Software Defined Networking has afforded numerous benefits to the network users but there are certain persisting issues with this technology, two of which are scalability and priva…
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
Akrit Mudvari, Antero Vainio, Iason Ofeidis +2
The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple ben…
A Quantitative Theory of Bottleneck Structures for Data Networks
Jordi Ros-Giralt, Noah Amsel, Sruthi Yellamraju +5
The conventional view of the congestion control problem in data networks is based on the principle that a flow's performance is uniquely determined by the state of its bottleneck l…