Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Reinforcement Learning-Based Precoding With Evolutionary Algorithms For 6G MIMO

Authors
Sivangi Ravikanth1, *, K. Prem Sagar2, Rajesh Kanuganti3, P. Sandeep4
1Department of Electronics and Communication Engineering, CVR College of Engineering, Mangalpalli , Telangana, India
2Department of Electronics and Communication Engineering, Mallareddy Engineering College and Management Sciences, Kistapur, Telangana, India
3Department of Electronics and Communication Engineering, Khammam Institute of Technology and Sciences, Khammam, Telangana, India
4Department of Electronics and Communication Engineering, Vignan Institute of Technology and Science, Deshmukhi, Hyderabad, Telangana, India
*Corresponding author. Email: sivangi.ravikanth25@gmail.com
Corresponding Author
Sivangi Ravikanth
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_94How to use a DOI?
Keywords
Reinforcement; Precoding; Massive MIMO; 6G; Machine Learning
Abstract

The rapid evolution of wireless communication technology has led to the emergence of sixth-generation (6G) networks, which require highly efficient Multiple-Input Multiple-Output (MIMO) systems. Precoding is a basic technique to improve spectral efficiency and mitigate interference in MIMO systems and thus is an important aspect to meet these requirements. In this paper, we propose a new reinforcement learning (RL)-based precoding scheme with evolutionary algorithms (EAs) to improve performance in 6G MIMO systems. The RL model is designed to learn dynamically optimal precoding policies, while evolutionary algorithms enable vast exploration and convergence to high-performance solutions. By virtue of the adaptive abilities of RL and the global search abilities of EAs, our approach achieves improved spectral efficiency, lower bit error rates, and better robustness against channel variations. Simulation results verify the performance excellence of the proposed hybrid approach over conventional precoding techniques and reveal its potential in next-generation wireless communication systems.

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 International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_94How 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  - Sivangi Ravikanth
AU  - K. Prem Sagar
AU  - Rajesh Kanuganti
AU  - P. Sandeep
PY  - 2025
DA  - 2025/11/04
TI  - Reinforcement Learning-Based Precoding With Evolutionary Algorithms For 6G MIMO
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1128
EP  - 1143
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_94
DO  - 10.2991/978-94-6463-858-5_94
ID  - Ravikanth2025
ER  -