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Original Article
Machine Learning Beam based optimization using Reinforcement Learning Techniques
Dr. T.C.Manjunath1
Bhuvan M2
Charan HG3
Deekshith H4
K R Naveen Gowda5
1 Dean of Research, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India. 2 3 4 5 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 162-171
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250506021
References
1. Parmida Geranmayeh and Eckhard Grass, “Machine Learning-Based Beam Selection for Maximizing Wireless Network Capacity,” IEEE Access, vol. 10, pp. 61241– 61254, 2022.
2. H. Ye, G. Y. Li, and B.-H. Juang, “Deep Reinforcement Learning Based Resource Allocation for V2V Communications,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3163–3173, Apr. 2019.
3. T. J. O’Shea and J. Hoydis, “An Introduction to Deep Learning for the Physical Layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563–575, Dec. 2017.
4. R. Ali et al., “Reinforcement Learning Enabled Massive Internet of Things for 6G Wireless Communications,” IEEE Commun. Standards Mag., 2021
5. C. Jiang and P. V. Schotten, “Machine Learning Paradigms for Next-Generation Wireless Networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98–105, Apr. 2017.
6. M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019.
7. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA: MIT Press, 2018.
8. B. Ali, F. Hussain, and H. K. Qureshi, “Q-Learning-Based Channel Access Scheme for Internet of Things,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10054– 10064, Oct. 2020.
9. A.Gupta and P. R. Kumar, “Learning-Based Beamforming and Power Control for 5G and Beyond,” IEEE Communications Magazine, vol. 58, no. 10, pp. 88– 93, Oct. 2020.
10. J. Wang, H. Zhu, and L. Dai, “Deep Learning for Wireless Physical Layer: Opportunities and Challenges,” China Communications, vol. 17, no. 11, pp. 92–111, Nov. 2020.
11. S. Tang, X. Zhang, and Y. Liu, “Energy-Efficient Beamforming in 6G Networks Using Multi-Agent Reinforcement Learning,” IEEE Internet of Things Journal, vol. 10, no. 7, pp. 6242–6255, Jul. 2023.
12. M. Z. Hassan, A. Mumtaz, and H. Gacanin, “A Survey on Reinforcement Learning for Beam Management in 5G and Beyond,” IEEE Access, vol. 9, pp. 123276–123300, 2021.
13. Streamlit Documentation, “Build Interactive Machine Learning Web Applications,” https://docs.streamlit.io/, Accessed: Nov. 2025.
14. NumPy Developers, “NumPy: Fundamental Package for Scientific Computing with Python,” https://numpy.org/, Accessed: Nov. 2025.
15. Matplotlib Developers, “Matplotlib: Visualization Library for Python,” https://matplotlib.org/, Accessed: Nov. 2025.
2. H. Ye, G. Y. Li, and B.-H. Juang, “Deep Reinforcement Learning Based Resource Allocation for V2V Communications,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3163–3173, Apr. 2019.
3. T. J. O’Shea and J. Hoydis, “An Introduction to Deep Learning for the Physical Layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563–575, Dec. 2017.
4. R. Ali et al., “Reinforcement Learning Enabled Massive Internet of Things for 6G Wireless Communications,” IEEE Commun. Standards Mag., 2021
5. C. Jiang and P. V. Schotten, “Machine Learning Paradigms for Next-Generation Wireless Networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98–105, Apr. 2017.
6. M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019.
7. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA: MIT Press, 2018.
8. B. Ali, F. Hussain, and H. K. Qureshi, “Q-Learning-Based Channel Access Scheme for Internet of Things,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10054– 10064, Oct. 2020.
9. A.Gupta and P. R. Kumar, “Learning-Based Beamforming and Power Control for 5G and Beyond,” IEEE Communications Magazine, vol. 58, no. 10, pp. 88– 93, Oct. 2020.
10. J. Wang, H. Zhu, and L. Dai, “Deep Learning for Wireless Physical Layer: Opportunities and Challenges,” China Communications, vol. 17, no. 11, pp. 92–111, Nov. 2020.
11. S. Tang, X. Zhang, and Y. Liu, “Energy-Efficient Beamforming in 6G Networks Using Multi-Agent Reinforcement Learning,” IEEE Internet of Things Journal, vol. 10, no. 7, pp. 6242–6255, Jul. 2023.
12. M. Z. Hassan, A. Mumtaz, and H. Gacanin, “A Survey on Reinforcement Learning for Beam Management in 5G and Beyond,” IEEE Access, vol. 9, pp. 123276–123300, 2021.
13. Streamlit Documentation, “Build Interactive Machine Learning Web Applications,” https://docs.streamlit.io/, Accessed: Nov. 2025.
14. NumPy Developers, “NumPy: Fundamental Package for Scientific Computing with Python,” https://numpy.org/, Accessed: Nov. 2025.
15. Matplotlib Developers, “Matplotlib: Visualization Library for Python,” https://matplotlib.org/, Accessed: Nov. 2025.
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