The proposed Q-learning optimized Internet of Things (IoT) integrated VLC system is highly suitable for indoor environments where electromagnetic interference (EMI), device density and real-time data transmission are critical challenges. Visible Light Communication (VLC) serves as an effective alternative to traditional RF communication by utilizing existing LED lighting infrastructure for dual-purpose illumination and data transmission. In such indoor settings the system enables real-time monitoring and secure transmission of vital information such as environmental data to an IoT-based cloud platform. The use of VLC in the visible light spectrum eliminates EMI making it ideal for environments where Radio Frequency (RF) communication is either restricted or unreliable. To adapt to dynamic indoor conditions such as human movement or changing light intensities the system employs Q-learning to continuously optimize transmission parameters like modulation index and power levels. This reinforcement learning approach ensures enhanced link reliability, reduced Bit Error Rate (BER) and stable communication under fluctuating indoor conditions. Additionally, the incorporation of Enhanced Asymmetrically Clipped Duty-Cycled Optical OFDM (EACDO-OFDM) improves spectral efficiency and power utilization thus making the solution energy-efficient and scalable for large-scale indoor IoT deployments. By integrating adaptive machine learning techniques with VLC and IoT the system delivers robust, intelligent and energy-conscious communication, well-suited for smart building automation, indoor sensing and real-time monitoring applications.
S. Dimitrov and H. Haas, “Principles of LED Light Communications,” Mar. 2015, doi: 10.1017/cbo9781107278929.
H. Haas, L. Yin, Y. Wang, and C. Chen, “What is LiFi?,” Journal of Lightwave Technology, vol. 34, no. 6, pp. 1533–1544, Mar. 2016, doi: 10.1109/jlt.2015.2510021.
P. H. Pathak, X. Feng, P. Hu, and P. Mohapatra, “Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2047–2077, 2015, doi: 10.1109/comst.2015.2476474.
S. Rajbhandari et al., “High-Speed Integrated Visible Light Communication System: Device Constraints and Design Considerations,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 9, pp. 1750–1757, Sep. 2015, doi: 10.1109/jsac.2015.2432551.
Y. Wang, B. Lin, and J. Ge, "OFDM-based Visible Light Communication for Indoor Healthcare Monitoring," in Proc. IEEE Int. Conf. on Communications (ICC), pp. 432–436 (2017).
T. Nguyen, H. Tran, and P. Yoo, "Leveraging VLC in IoT-based Smart Hospitals," in Proc. IEEE Global Communications Conference (GLOBECOM), pp. 1–5 (2018).
H. Elgala, R. Mesleh, and H. Haas, “A study of LED nonlinearity effects on optical wireless transmission using OFDM,” 2009 IFIP International Conference on Wireless and Optical Communications Networks, Apr. 2009, doi: 10.1109/wocn.2009.5010576.
Zhang, W., Wang, Y., & Haas, H, “Improved adaptive loading algorithm for DCO-OFDM in LiFi systems”, IEEE Photonics Technology Letters, 29(5), 418–421, DOI: 10.1109/LPT.2017.2650220 (2017).
I. E. Shaalan, E. M. Fadly, and M. H. Aly, “Enhanced ADO-OFDM-based adaptive digital dimming VLC system,” Optics Letters, vol. 47, no. 9, p. 2133, Apr. 2022, doi: 10.1364/ol.454339.
S. Sinha and A. Chockalingam, “OTFS Modulation in Dual-LED Indoor Visible Light Communication Systems,” 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), pp. 1–7, Sep. 2021, doi: 10.1109/vtc2021-fall52928.2021.9625256.
J. Vidhya, R. P. Vishnu, and E. C. Britto, “Analysis of a visible light communication system with QAM-OFDM modulated white LEDs using FSO,” Applied Optics, vol. 63, no. 18, p. 4830, Jun. 2024, doi: 10.1364/ao.524042.
A. M. J. Kinol, R. M. Bommi, P. M. Krishnammal, and G. Uganya, “Assessing BER Reduction in VLC Systems Using Advanced QAM Techniques,” 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 203–206, Aug. 2024, doi: 10.1109/icesc60852.2024.10689735.
M. Alharbi, S. Neelakandan, S. Gupta, R. Saravanakumar, S. Kiran, and A. Mohan, “Mobility aware load balancing using Kho–Kho optimization algorithm for hybrid Li-Fi and Wi-Fi network,” Wireless Networks, vol. 30, no. 6, pp. 5111–5125, Jan. 2023, doi: 10.1007/s11276-022-03225-0.
A. H. Victoria, N. M. Devarajan, R. Saravanakumar, K. Sekaran, C. Singh, and V. Suneetha, “Spectral efficiency enhancement by hybrid pre-coding technique for reconfigurable intelligent surfaces-based massive MIMO systems under variable CSI,” Soft Computing, vol. 28, no. 23–24, pp. 13909–13916, May 2023, doi: 10.1007/s00500-023-08464-7.
S. Govindaraj, L. Raja, S. Velmurugan, and K. Vijayalakshmi, “Extreme learning machine optimized by artificial cell swarm optimization for the data fusion modal in WSNs,” Peer-to-Peer Networking and Applications, vol. 17, no. 3, pp. 1344–1357, Feb. 2024, doi: 10.1007/s12083-024-01643-9.
D. Tsonev, S. Videv, and H. Haas, “Unlocking Spectral Efficiency in Intensity Modulation and Direct Detection Systems,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 9, pp. 1758–1770, Sep. 2015, doi: 10.1109/jsac.2015.2432530.
R. Mesleh, H. Elgala, and H. Haas, “On the Performance of Different OFDM Based Optical Wireless Communication Systems,” Journal of Optical Communications and Networking, vol. 3, no. 8, p. 620, Jul. 2011, doi: 10.1364/jocn.3.000620.
S. M. Hameed, S. M. Abdulsatar, and A. A. Sabri, “Visible light communication performance using MIMO‐ADO‐OFDM system,” Transactions on Emerging Telecommunications Technologies, vol. 33, no. 7, Mar. 2022, doi: 10.1002/ett.4493.
H. Z. Dhaam and F. M. Ali, “Optical GFDM for indoor visible light communication: a comprehensive review and future outlook,” Journal of Optics, Aug. 2024, doi: 10.1007/s12596-024-02061-z.
V. Kavitha, B. A. Vijayalakshmi, M. VanithaLakshmi, and J. wankhede, “VLC-based DCO-OFDM and WSN in hospitals for medical information transmission,” Journal of Optics, vol. 53, no. 5, pp. 4988–4996, Mar. 2024, doi: 10.1007/s12596-024-01729-w.
D. S, M. D, A. kumar L, and R. V, “Optimized Medical Data Transmission Using OFDM VLC and Reinforcement Learning in Remote Health Monitoring,” Journal of Machine and Computing, pp. 1915–1930, Jul. 2025, doi: 10.53759/7669/jmc202505150.
B. Anitha Vijayalakshmi, A. Senthil Kumar, V. Kavitha, and D. Ravikumar, “Transmitting patient’s health care information using LEDs in hospitals through VLC technology,” Journal of Optics, vol. 53, no. 5, pp. 4623–4630, Feb. 2024, doi: 10.1007/s12596-023-01650-8.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Anitha Vijayalakshmi B, Chin-Shiuh Shieh, Mong-Fong Horng and Mary Victoria R;
Writing- Original Draft Preparation: Anitha Vijayalakshmi B, Chin-Shiuh Shieh, Mong-Fong Horng and Mary Victoria R;
Visualization: Anitha Vijayalakshmi B, Chin-Shiuh Shieh;
Investigation: Mong-Fong Horng and Mary Victoria R;
Supervision: Anitha Vijayalakshmi B, Chin-Shiuh Shieh;
Validation: Mong-Fong Horng and Mary Victoria R;
Writing- Reviewing and Editing: Anitha Vijayalakshmi B, Chin-Shiuh Shieh, Mong-Fong Horng and Mary Victoria R; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Anitha Vijayalakshmi B
SIMATS Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Anitha Vijayalakshmi B, Chin-Shiuh Shieh, Mong-Fong Horng and Mary Victoria R, “Q Learning Optimized EACDO OFDM and IoT Framework for VLC Enabled Smart Indoor Infrastructure”, Journal of Machine and Computing, vol.5, no.4, pp. 2305-2315, October 2025, doi: 10.53759/7669/jmc202505179.
