A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization

Ilias Crysovergis; Stylianos E. Trevlakis; Dimitris Kleitsas; Alexandros Apostolos A. Boulogeorgos; Theodoros A. Tsiftsis; Dusit Niyato

doi:10.1109/ICMLCN64995.2025.11140517

A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization

Ilias Crysovergis^*
, Stylianos E. Trevlakis
, Dimitris Kleitsas
, Alexandros Apostolos A. Boulogeorgos
, Theodoros A. Tsiftsis
, Dusit Niyato

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

This demo presents a digital twin of a reconfigurable intelligent surface-empowered wireless system that employs spiking reinforcement learning (SRL) optimization policy for phase adaptation in order to maximize the network coverage, while minimizing the energy consumption at both the microcontroller and transmission related processes. The demo assesses the efficiency of SRL against conventional deep reinforcement learning approaches in terms of (i) energy consumption, (ii) reduction of training latency, (iii) probability of outage, and (iv) bit error rate.

Original language	English
Title of host publication	2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331520427
DOIs	https://doi.org/10.1109/ICMLCN64995.2025.11140517
Publication status	Published - 2025
Externally published	Yes
Event	2nd IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025 - Barcelona, Spain Duration: 26 May 2025 → 29 May 2025

Publication series

Name	2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025

Conference

Conference	2nd IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025
Country/Territory	Spain
City	Barcelona
Period	26/05/25 → 29/05/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Free Keywords

Digital twin (DT)
deep reinforcement learning (DRL)
spiking neural networks (SNN)
spiking reinforcement learning (SNL)

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Signal Processing
Control and Optimization

Access to Document

10.1109/ICMLCN64995.2025.11140517

Cite this

Crysovergis, I., Trevlakis, S. E., Kleitsas, D., Boulogeorgos, A. A. A., Tsiftsis, T. A., & Niyato, D. (2025). A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization. In 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025 (2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLCN64995.2025.11140517

Crysovergis, Ilias ; Trevlakis, Stylianos E. ; Kleitsas, Dimitris et al. / A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization. 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025. Institute of Electrical and Electronics Engineers Inc., 2025. (2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025).

@inproceedings{55fcdab89a5e460398cd176cc297a4e4,

title = "A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization",

abstract = "This demo presents a digital twin of a reconfigurable intelligent surface-empowered wireless system that employs spiking reinforcement learning (SRL) optimization policy for phase adaptation in order to maximize the network coverage, while minimizing the energy consumption at both the microcontroller and transmission related processes. The demo assesses the efficiency of SRL against conventional deep reinforcement learning approaches in terms of (i) energy consumption, (ii) reduction of training latency, (iii) probability of outage, and (iv) bit error rate.",

keywords = "Digital twin (DT), deep reinforcement learning (DRL), spiking neural networks (SNN), spiking reinforcement learning (SNL)",

author = "Ilias Crysovergis and Trevlakis, \{Stylianos E.\} and Dimitris Kleitsas and Boulogeorgos, \{Alexandros Apostolos A.\} and Tsiftsis, \{Theodoros A.\} and Dusit Niyato",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2nd IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025 ; Conference date: 26-05-2025 Through 29-05-2025",

year = "2025",

doi = "10.1109/ICMLCN64995.2025.11140517",

language = "English",

series = "2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025",

address = "United States",

}

Crysovergis, I, Trevlakis, SE, Kleitsas, D, Boulogeorgos, AAA, Tsiftsis, TA & Niyato, D 2025, A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization. in 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025. 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025, Barcelona, Spain, 26/05/25. https://doi.org/10.1109/ICMLCN64995.2025.11140517

A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization. / Crysovergis, Ilias; Trevlakis, Stylianos E.; Kleitsas, Dimitris et al.
2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025. Institute of Electrical and Electronics Engineers Inc., 2025. (2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization

AU - Crysovergis, Ilias

AU - Trevlakis, Stylianos E.

AU - Kleitsas, Dimitris

AU - Boulogeorgos, Alexandros Apostolos A.

AU - Tsiftsis, Theodoros A.

AU - Niyato, Dusit

PY - 2025

Y1 - 2025

N2 - This demo presents a digital twin of a reconfigurable intelligent surface-empowered wireless system that employs spiking reinforcement learning (SRL) optimization policy for phase adaptation in order to maximize the network coverage, while minimizing the energy consumption at both the microcontroller and transmission related processes. The demo assesses the efficiency of SRL against conventional deep reinforcement learning approaches in terms of (i) energy consumption, (ii) reduction of training latency, (iii) probability of outage, and (iv) bit error rate.

AB - This demo presents a digital twin of a reconfigurable intelligent surface-empowered wireless system that employs spiking reinforcement learning (SRL) optimization policy for phase adaptation in order to maximize the network coverage, while minimizing the energy consumption at both the microcontroller and transmission related processes. The demo assesses the efficiency of SRL against conventional deep reinforcement learning approaches in terms of (i) energy consumption, (ii) reduction of training latency, (iii) probability of outage, and (iv) bit error rate.

KW - Digital twin (DT)

KW - deep reinforcement learning (DRL)

KW - spiking neural networks (SNN)

KW - spiking reinforcement learning (SNL)

UR - https://www.scopus.com/pages/publications/105016787360

U2 - 10.1109/ICMLCN64995.2025.11140517

DO - 10.1109/ICMLCN64995.2025.11140517

M3 - Conference contribution

AN - SCOPUS:105016787360

T3 - 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025

BT - 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025

Y2 - 26 May 2025 through 29 May 2025

ER -

Crysovergis I, Trevlakis SE, Kleitsas D, Boulogeorgos AAA, Tsiftsis TA, Niyato D. A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization. In 2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025. Institute of Electrical and Electronics Engineers Inc. 2025. (2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025). doi: 10.1109/ICMLCN64995.2025.11140517

A Digital Twin Based Reconfigurable Intelligent Surface Phase Adaptation Using Spiking Reinforcement Learning Policy Optimization

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

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Cite this