Publications
All publications from our lab, ordered by most recent.
MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks
S. Nikolaidis, S. I. Venieris, L. Malachias and I. S. Venieris, “MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks,” in IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2026), 2026, doi: https://doi.org/10.48550/arXiv.2609.14660.
On the Ergodic Capacity for SIM-Aided Holographic MIMO Communications
A. Papazafeiropoulos, I. Bartsiokas, D. I. Kaklamani and I. S. Venieris, “On the Ergodic Capacity for SIM-Aided Holographic MIMO Communications,” IEEE Wireless Communications Letters, vol. 15, pp. 1120-1124, 2026, doi: https://doi.org/10.1109/lwc.2025.3649381.
Design, Analysis and Experimental Characterization of a Passive X-Band I/Q Downconversion Subsystem for Maritime Radar Applications
T. Bountas, Y. E. Stratakos, D. Kaklamani and I. Papananos, “Design, Analysis and Experimental Characterization of a Passive X-Band I/Q Downconversion Subsystem for Maritime Radar Applications,” Electronics (MDPI), vol. 15, no. 13, pp. 2834, June 2026, doi: https://doi.org/10.3390/electronics15132834.
Ergodic Mutual Information and Outage Probability for SIM-Assisted Holographic MIMO Communications
A. Papazafeiropoulos, P. Kourtessis, D. I. Kaklamani and I. S. Venieris, “Ergodic Mutual Information and Outage Probability for SIM-Assisted Holographic MIMO Communications,” IEEE Transactions on Vehicular Technology, vol. 75, no. 5, pp. 7703-7715, May 2026, doi: https://doi.org/10.1109/tvt.2025.3625663.
Flexible Intelligent Metasurface for Downlink Communications Under Statistical CSI
V. Kumar, A. Papazafeiropoulos, P. Kourtessis, J. Senior, M. Chafii, D. I. Kaklamani and I. S. Venieris, “Flexible Intelligent Metasurface for Downlink Communications Under Statistical CSI,” IEEE Wireless Communications Letters, vol. 15, pp. 1150-1154, 2026, doi: https://doi.org/10.1109/lwc.2025.3649732.
Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks
M. L. Bartsioka, I. A. Bartsiokas, A. D. Panagopoulos, D. I. Kaklamani and I. S. Venieris, “Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks,” in International Applied Computational Electromagnetics Society (ACES) Symposium, 2026, doi: https://doi.org/10.48550/arXiv.2603.10983.
FRIEND: Federated Learning for Joint Optimization of multi-RIS Configuration and Eavesdropper Intelligent Detection in B5G Networks
M. L. A. Bartsioka, I. A. Bartsiokas, A. K. Papazafeiropoulos, M. A. Seimeni, D. I. Kaklamani and I. S. Venieris, “FRIEND: Federated Learning for Joint Optimization of multi-RIS Configuration and Eavesdropper Intelligent Detection in B5G Networks,” in 29th Conference on Innovation in Clouds, Internet and Networks (ICIN 2026), 2026, doi: https://doi.org/10.48550/arXiv.2603.10977.
A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation
I. Panopoulos, M. L. A. Bartsioka, S. Nikolaidis, S. I. Venieris, D. I. Kaklamani and I. S. Venieris, “A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation,” ACM Transactions on AI Security and Privacy, April 2026, doi: https://doi.org/10.48550/arXiv.2604.21623.
Intelligent Multi-Objective Path Planning for Unmanned Surface Vehicles via Deep and Fuzzy Reinforcement Learning
I. A. Bartsiokas, C. Ntakolia, G. Avdikos and D. Lyridis, “Intelligent Multi-Objective Path Planning for Unmanned Surface Vehicles via Deep and Fuzzy Reinforcement Learning,” Journal of Marine Science and Engineering (MDPI), vol. 13, no. 12, pp. 2285, November 2025, doi: https://doi.org/10.3390/jmse13122285.
Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
I. Panopoulos, M.-L. A. Bartsioka, S. Nikolaidis, S. I. Venieris, D. I. Kaklamani and I. S. Venieris, “Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT,” in 10th International Conference on Smart and Sustainable Technologies (SpliTech 2025), 2025, doi: https://doi.org/10.48550/arXiv.2506.18114.
STARS-Enabled Full-Duplex Two-Way mMIMO System Under Spatially-Correlated Channels
A. Papazafeiropoulos, P. Kourtessis, S. Chatzinotas, D. I. Kaklamani and I. S. Venieris, “STARS-Enabled Full-Duplex Two-Way mMIMO System Under Spatially-Correlated Channels,” IEEE Transactions on Vehicular Technology, vol. 74, no. 7, pp. 10495-10509, July 2025, doi: https://doi.org/10.1109/TVT.2025.3543646.
Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading Channels
A. Papazafeiropoulos, P. Kourtessis, D. I. Kaklamani and I. S. Venieris, “Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading Channels,” IEEE Wireless Communications Letters, vol. 14, no. 5, May 2025, doi: https://doi.org/10.48550/arXiv.2502.12692.
Service Orchestration at the Extreme-Edge: An Experimental Investigation Over a 5G Testbed
G. Drainakis, P. Pantazopoulos, K. V. Katsaros, V. Sourlas, T. Xirofotos, N. Baganal-Krishna, A. Rizk, R. Horvath, G. Scivoletto, A. Amditis and D. I. Kaklamani, “Service Orchestration at the Extreme-Edge: An Experimental Investigation Over a 5G Testbed,” in ICC 2025 - IEEE International Conference on Communications, pp. 844-849, June 2025, doi: https://doi.org/10.1109/ICC52391.2025.11160988.
A Federated Learning Scheme for Eavesdropper Detection in B5G – IIoT Network Orientations
M. L. A. Bartsioka, I. A. Bartsiokas, P. K. Gkonis, A. K. Papazafeiropoulos, D.-T. I. Kaklamani, I. S. Venieris, “A Federated Learning Scheme for Eavesdropper Detection in B5G – IIoT Network Orientations,” in IEEE Open Journal of the Communications Society ( Volume: 6), 2025, doi: https://doi.org/10.1109/OJCOMS.2025.3599446.
ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks
M.-L. A. Bartsioka, I. A. Bartsiokas, P. K. Gkonis, D. I. Kaklamani and I. S. Venieris, “ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks,” in 2025 IEEE Symposium on Computers and Communications (ISCC), pp. 1-6, July 2025, doi: https://doi.org/10.1109/ISCC65549.2025.11326255.
AI-Driven RF Fingerprinting for Secure Positioning Optimization in 6G Networks
I. A. Bartsiokas, M.-L. A. Bartsioka, A. K. Papazafeiropoulos, D. I. Kaklamani and I. S. Venieris, “AI-Driven RF Fingerprinting for Secure Positioning Optimization in 6G Networks,” Microwave (MDPI), vol. 2, no. 1, pp. 1, December 2025, doi: https://doi.org/10.3390/microwave2010001.
Federated Learning for 6G HetNets’ Physical Layer Optimization: Perspectives, Trends, and Challenges
I. Bartsiokas, P. Gkonis, A. K. Papazafeiropoulos, D. I. Kaklamani and I. S. Venieris, “Federated Learning for 6G HetNets’ Physical Layer Optimization: Perspectives, Trends, and Challenges,” in Encyclopedia of Information Science and Technology, Sixth Edition. IGI Global, pp. 1-28, 2025, doi: https://doi.org/10.4018/978-1-6684-7366-5.ch070.
Performance of Double-Stacked Intelligent Metasurface-Assisted Multiuser Massive MIMO Communications in the Wave Domain
A. K. Papazafeiropoulos, P. Kourtessis, S. Chatzinotas, D. I. Kaklamani and I. S. Venieris, “Performance of Double-Stacked Intelligent Metasurface-Assisted Multiuser Massive MIMO Communications in the Wave Domain,” IEEE Transactions on Wireless Communications, vol. 24, no. 5, pp. 4205-4218, May 2024, doi: https://doi.org/10.1109/twc.2025.3533913.
NordicDat: A Cross-Border Predictive QoS Dataset
T. Miekkala, P. Pyykonen, G. Drainakis, P. Pantazopoulos, T. Muller, K. V. Katsaros, V. Sourlas, A. Amditis and D. I. Kaklamani, “NordicDat: A Cross-Border Predictive QoS Dataset,” in IEEE Global Communications Conference (IEEE Globecom), pp. 1281-1286, December 2024, doi: https://doi.org/10.1109/globecom52923.2024.10901462.
Near-Field Beamforming for Stacked Intelligent Metasurfaces-Assisted MIMO Networks
A. Papazafeiropoulos, P. Kourtessis, S. Chatzinotas, D. I. Kaklamani and I. S. Venieris, “Near-Field Beamforming for Stacked Intelligent Metasurfaces-Assisted MIMO Networks,” IEEE Wireless Communications Letters, vol. 13, no. 11, pp. 3035-3039, November 2024, doi: https://doi.org/10.1109/LWC.2024.3438840.