Publications
All publications from our lab, ordered by most recent.
Experimental Assessment of Low-Pressure Plasma Interference-Switch Performance in MW-Class Microwave Pulse Compressor
V. A. Tantanis, S. P. Savaidis, Z. C. Ioannidis, D. I. Kaklamani and N. K. Uzunoglu, “Experimental Assessment of Low-Pressure Plasma Interference-Switch Performance in MW-Class Microwave Pulse Compressor,” IEEE Transactions on Plasma Science, vol. 52, no. 7, pp. 2676-2685, July 2024, doi: https://doi.org/10.1109/TPS.2024.3445718.
Effect of Channel Aging on Beyond Diagonal Reconfigurable Intelligent Surfaces
A. Papazafeiropoulos, P. Kourtessis and S. Chatzinotas, “Effect of Channel Aging on Beyond Diagonal Reconfigurable Intelligent Surfaces,” IEEE Open Journal of the Communications Society, vol. 5, pp. 6303-6313, 2024, doi: https://doi.org/10.1109/OJCOMS.2024.3460055.
A Federated Learning-based resource allocation scheme for relaying-assisted communications in multicellular next generation network topologies
I. Bartsiokas, P. Gkonis, D. I. Kaklamani and I. S. Venieris, “A Federated Learning-based resource allocation scheme for relaying-assisted communications in multicellular next generation network topologies,” Electronics MDPI, Special Issue: “Feature Papers in Microwave and Wireless Communications Section”, vol. 13, no. 2, pp. 390, January 2024, doi: https://doi.org/10.3390/electronics13020390.
Distributed Predictive QoS in Automotive Environments Under Concept Drift
G. Drainakis, P. Pantazopoulos, K. V. Katsaros, V. Sourlas, A. Amditis and D. I. Kaklamani, “Distributed Predictive QoS in Automotive Environments Under Concept Drift,” in The International Federation for Information Processing (IFIP) Networking, pp. 549-554, June 2024, doi: https://ieeexplore.ieee.org/document/10619805.
CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads
I. Panopoulos, S. I. Venieris and I. S. Venieris, “CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads,” ACM Transactions on Embedded Computing Systems, vol. 23, no. 4, pp. 32, July 2024, doi: https://doi.org/10.1145/3665868.
Achievable Rate Optimization for Large Stacked Intelligent Metasurfaces Based on Statistical CSI
A. K. Papazafeiropoulos, P. Kourtessis, S. Chatzinotas, D. I. Kaklamani and I. S. Venieris, “Achievable Rate Optimization for Large Stacked Intelligent Metasurfaces Based on Statistical CSI,” IEEE Wireless Commun. Lett, vol. 13, no. 9, pp. 2337-2341, September 2024, doi: https://ieeexplore.ieee.org/document/10543143/.
MultiTASC++: A continuously adaptive scheduler for edge-based multi-device cascade inference
S. Nikolaidis, S. I. Venieris and I. S. Venieris, “MultiTASC++: A continuously adaptive scheduler for edge-based multi-device cascade inference,” ITU Journal on Future of Evolving Technologies, vol. 5, no. 1, pp. 26-46, March 2024, doi: https://doi.org/10.52953/TBYB6219.
A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks
I. Bartsiokas, P. Gkonis, D. I. Kaklamani and I. S. Venieris, “A DL-Enabled Relay Node Placement and Selection Framework in Multicellular Networks,” IEEE Access, vol. 11, pp. 65153-65169, 2023, doi: https://doi.org/10.1109/access.2023.3290482.
From centralized to Federated Learning: Exploring performance and end-to-end resource consumption
G. Drainakis, P. Pantazopoulos, K. V. Katsaros, V. Sourlas, A. Amditis and D. I. Kaklamani, “From centralized to Federated Learning: Exploring performance and end-to-end resource consumption,” Computer Networks, vol. 225, pp. 109657, April 2023, doi: https://doi.org/10.1016/j.comnet.2023.109657.
Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices
I. Panopoulos, S. Nikolaidis, S. I. Venieris and I. S. Venieris, “Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices,” in 3rd IEEE International Workshop on Distributed Intelligent Systems (DistInSys), pp. 1-4, July 2023, doi: https://doi.org/10.1109/iscc58397.2023.10217850.
MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge
S. Nikolaidis, S. I. Venieris and I. S. Venieris, “MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge,” in 28th IEEE Symposium on Computers and Communications (ISCC) Awards:Best Student Full Paper Award, July 2023, doi: https://ieeexplore.ieee.org/document/10217872/.
On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission
M. Seimeni, P. Alevizaki, P. Gkonis, D. I. Kaklamani and I. S. Venieris, “On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission,” Physical Communication ISSN:1874-4907, pp. 101725, 2022, doi: https://www.sciencedirect.com/science/article/abs/pii/S1874490722000696?via%3Dihub.
ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey
I. Bartsiokas, P. Gkonis, D. I. Kaklamani and I. S. Venieris, “ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey,” IEEE Access, vol. 10, pp. 83507-83528, 2022, doi: https://ieeexplore.ieee.org/abstract/document/9851417.
On Relay-Based Subcarrier Allocation and Power Management in 5G Multicellular Networks
K. A. Psilopanagiotis, I. Bartsiokas, P. Gkonis and D. I. Kaklamani, “On Relay-Based Subcarrier Allocation and Power Management in 5G Multicellular Networks,” in IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), pp. 1-6, 2022, doi: https://ieeexplore.ieee.org/abstract/document/9861029.
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
S. I. Venieris, I. Panopoulos and I. S. Venieris, “How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures,” in 32nd IEEE International Conference on Application-Specific Systems, Architectures and Processors (ASAP), pp. 93-100, July 2021, doi: https://doi.org/10.1109/asap52443.2021.00022.
OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices
S. I. Venieris, I. Panopoulos and I. S. Venieris, “OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices,” in 7th IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1-8, August 2021, doi: https://doi.org/10.1109/smartcomp52413.2021.00021.
Radio Network Planning Towards 5G mmWave Standalone Small-cell Architectures
G. E. Athanasiadou, P. Fytampanis, D. A. Zarbouti, G. V. Tsoulos, P. Gkonis and D. I. Kaklamani, “Radio Network Planning Towards 5G mmWave Standalone Small-cell Architectures,” Electronics ISSN: 2079-9292, vol. 9, no. 2, pp. 339, February 2020, doi: https://doi.org/10.3390/electronics9020339.
A Comprehensive Study on Simulation Techniques for 5G Networks: State of the Art Results, Analysis, and Future Challenges
P. Gkonis, P. T. Trakadas and D. I. Kaklamani, “A Comprehensive Study on Simulation Techniques for 5G Networks: State of the Art Results, Analysis, and Future Challenges,” Electronics ISSN: 2079-9292, vol. 9, no. 3, pp. 468, March 2020, doi: https://doi.org/10.3390/electronics9030468.