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Machine Learning Beam based optimization using Reinforcement Learning Techniques
Authors
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.
Article Information
- Published In
- Volume 5, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 162-171
- Article Type
- Original Article
- Check for updates
-
References
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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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