Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)

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A Reinforcement Learning–Enabled Digital Twin Framework for Mobile Network Infrastructure

Authors
Shahnaz Fatima1, *, Mohammad Rafee Shaik2, M. Ravi Sankar3, K. Sathish Kumar4, Tamalam Manogna5, Salar Mohammad6
1Department of Electronics and Communications and Engineering, Sasi Institute of Technology and Engineering, Tadepalligudem, AP, India
2Department of Artifitial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering, Tadepalligudem, AP, India
3Department of Electronics and Communications Engineering, Sasi Institute of Technology and Engineering, Tadepalligudem, AP, India
4Department of Electrical and Electronics Engineering, Vidya Jyothi Institute of Technology, Hyderabad, Telangana, India
5Department of Electronics and Communications and Engineering, Andhra Loyola Institute of Engineering and Technology, Vijayawada, India
6Department of Data Science, Anurag University, Hyderabad, Telangana, India
*Corresponding author. Email: shahnaz1981fat@gmail.com
Corresponding Author
Shahnaz Fatima
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-940-7_3How to use a DOI?
Keywords
Digital Twin; mobile networks; reinforcement learning; network optimization
Abstract

Rising network complexity in next-generation systems underscores the shortcomings of traditional optimization methods in both modeling and algorithms. This paper introduces a digital twin for mobile network (DTMN) architecture tailored for 6G networks. To overcome the limitations of traditional optimization methods, the DTMN adopts a simulation–optimization framework. The network simulation engine provides feedback that validates optimizer decisions and is used to iteratively train ML-based optimizers. In practice, we implement a prototype system that leverages data-driven technologies for modelling network elements, environments and the BSs. Built upon the deployed DTMN prototype, the ML network optimizer provides ideal network configuration solutions. Experimental results show that our DTMN delivers solutions for optimizing networks in the real world.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 December 2025
ISBN
978-94-6463-940-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-940-7_3How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shahnaz Fatima
AU  - Mohammad Rafee Shaik
AU  - M. Ravi Sankar
AU  - K. Sathish Kumar
AU  - Tamalam Manogna
AU  - Salar Mohammad
PY  - 2025
DA  - 2025/12/31
TI  - A Reinforcement Learning–Enabled Digital Twin Framework for Mobile Network Infrastructure
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 16
EP  - 25
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_3
DO  - 10.2991/978-94-6463-940-7_3
ID  - Fatima2025
ER  -