Efficient hybrid signal detection for optical NOMA with 512-QAM in visible light communication
-
Arun Kumar
,
Chandrashekhara K T
Abstract
This work presents a hybrid signal detection scheme for optical non-orthogonal multiple access (NOMA) systems using 512-QAM modulation in visible light communication (VLC) networks. As future VLC systems demand ultra-high data rates and superior spectral efficiency, they require high-order modulation like 512-QAM, which is highly susceptible to noise, nonlinearities, and channel distortions, complicating reliable detection. To tackle these challenges, a hybrid detection strategy combining traditional algorithms – such as minimum mean square error (MMSE) and zero-forcing (ZF) – with advanced machine learning techniques like deep neural networks (DNNs) is proposed. This approach harnesses the low complexity and speed of conventional detectors alongside the adaptive learning and nonlinear handling capabilities of DNNs, enabling robust detection under challenging conditions. Simulations conducted under Rayleigh fading in a MIMO-VLC setup using MATLAB 2016 extensively evaluate BER and PSD performance. Results demonstrate that the hybrid model achieves the target BER of 10−3 with up to a 13 dB SNR improvement over traditional methods. Moreover, it ensures better spectral containment, minimizing interference and enhancing bandwidth utilization. These outcomes highlight the potential of hybrid detection schemes to realize high-capacity, energy-efficient, and reliable VLC systems, making them suitable for smart indoor environments such as wireless data hubs, augmented reality, and smart lighting in hospitals and intelligent buildings.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
-
Conflict of interest: The authors state no conflict of interest.
-
Research funding: Not applicable.
-
Data availability: Not applicable.
References
1. Kumar, A, Nanthaamornphong, A. Lowering the PAPR of the optical OTFS-based 6G radio with a hybrid PTS-PSO genetic approach. Discov Appl Sci 2024;6:574. https://doi.org/10.1007/s42452-024-06292-4.Suche in Google Scholar
2. Lv, S, Zhao, J, Yang, L, Li, Q. Genetic algorithm based bilayer PTS scheme for peak-to-average power ratio reduction of FBMC/OQAM signal. IEEE Access 2020;8:17945–55. https://doi.org/10.1109/ACCESS.2020.2967846.Suche in Google Scholar
3. Tuli, EA, Akter, R, Lee, JM, Kim, D-S. Whale optimization-based PTS scheme for PAPR reduction in UFMC systems. IET Commun 2024;18:187–95. https://doi.org/10.1049/cmu2.12708.Suche in Google Scholar
4. Kiambi, S, Mwangi, E, Kamucha, G. Reducing PAPR of OFDM signals using a tone reservation method based on l-norm minimization. J Elect Sys Inf Technol 2022;9:12. https://doi.org/10.1186/s43067-022-00055-0.Suche in Google Scholar
5. Chu, TMC, Zepernick, HJ, Westerhagen, A, Höök, A, Granbom, B. Performance assessment of OTFS modulation in high doppler airborne communication networks. Mobile Network Appl 2022;27:1746–56. https://doi.org/10.1007/s11036-022-01928-4.Suche in Google Scholar
6. Janssen, AJEM. The Zak transform: a signal transform for sampled time-continuous signals. Philips J Res 1988;43:23–69.Suche in Google Scholar
7. Gunturu, A, Godala, AR, Sahoo, AK, Chavva, AKR. Performance analysis of OTFS waveform for 5G NR mmWave communication system. In: Proceedings of IEEE Wireless Communications and Networking Conference, Nanjing, China; 2021:1–6 pp.10.1109/WCNC49053.2021.9417346Suche in Google Scholar
8. Cheng, J, Jia, C, Gao, H, Xu, W, Bie, Z. OTFS based receiver scheme with multi-antennas in high-mobility V2X systems. In: Proceedings of IEEE International Conference on Communication Workshops, Dublin, Ireland; 2020:1–6 pp.10.1109/ICCWorkshops49005.2020.9145313Suche in Google Scholar
9. Kumar, A, Gaur, N, Aly, AA, Nanthaamornphong, A. PAPR reduction of OTFS using an automatic amplitude reduction neural network with vendermonde matrix-based PTS and SLM algorithms. J Wireless Commun Network 2024, 84 (2024). https://doi.org/10.1186/s13638-024-02414-zSuche in Google Scholar
10. Mhatre, K, Khot, UP. Efficient selective mapping PAPR reduction technique. Procedia Comput Sci 2015;45:620–7. https://doi.org/10.1016/j.procs.2015.03.117.Suche in Google Scholar
11. Sohn, I, Kim, SC. Neural network based simplified clipping and filtering technique for PAPR reduction of OFDM signals. IEEE Commun Lett 2015;19:1438–41. https://doi.org/10.1109/lcomm.2015.2441065.Suche in Google Scholar
12. Kumar, A, Gaur, N, Aziz, N. Signal detection of M-MIMO-orthogonal time frequency space modulation using hybrid algorithms: ZFE + MMSE and ZFE + MF. Results Eng 2024;24. https://doi.org/10.1016/j.rineng.2024.103311.Suche in Google Scholar
13. Kumar, A, Gaur, N, Nanthaamornphong, A. Machine learning RNNs, SVM and NN algorithm for Massive-MIMO-OTFS 6G waveform with Rician and Rayleigh channel. Egypt Inf J 2024;27. https://doi.org/10.1016/j.eij.2024.100531.Suche in Google Scholar
14. Chataut, R, Akl, R. Massive MIMO systems for 5G and beyond networks – overview, recent trends, challenges, and future research direction. Sensors 2020;20:1–35. https://doi.org/10.3390/s20102753.Suche in Google Scholar PubMed PubMed Central
15. Kumar, A. Detection in 5G mobile communication system using hybrid technique. Natl Acad Sci Lett 2021;44:39–42. https://doi.org/10.1007/s40009-020-00962-8.Suche in Google Scholar
16. Aziz, N, Kumar, A, Alamro, H, Alruwais, N, Allafi, R, Nemri, N, et al.. Enhancing OTFS modulation for 6G through hybrid PAPR reduction technique for different sub-carriers. Fractals 2024;32:2540014. https://doi.org/10.1142/s0218348x25400146.Suche in Google Scholar
17. Kumar, A, Maashi, M, Alshahrani, HM, Arasi, MA, Yahya, AE, Nanthaamornphong, A, et al.. Peak to average power computing and optimization of optical OTFS 5G waveform using hybrid fractal-based signal processing algorithm. Fractals 2024;32:2540040. https://doi.org/10.1142/s0218348x25400407.Suche in Google Scholar
18. Alanazi, MH, Kumar, A, Aljebreen, M, Alzaben, N, Nanthaamornphong, A, Maray, M, et al.. Reducing PAPR in OTFS 6G waveforms using particle swarm optimization-based PTS and SLM techniques with 64, 256, and 512 sub-carriers in Rician and Rayleigh channels. Fractals 2024;32:2540018. https://doi.org/10.1142/s0218348x25400183.Suche in Google Scholar
19. Islam, MM, Islam, MA, Ahmed, MF. A DNN-based 5G MIMO system adopting a mix of tactics. Discov Electron 2025. https://doi.org/10.1007/s44291-025-00055-0.Suche in Google Scholar
20. Mohammed, SK, Hadani, R, Chockalingam, A, Calderbank, R. OTFS – a mathematical foundation for communication and radar sensing in the delay-Doppler domain. IEEE BITS Inf Theory Magazine 2022;2:36–55. https://doi.org/10.1109/MBITS.2022.3216536.Suche in Google Scholar
21. Okoyeigbo, O, Deng, X, Imoize, AL, Shobayo, O. OTFS: a potential waveform for space–air–ground integrated networks in 6G and beyond. Telecom 2025;6:19. https://doi.org/10.3390/telecom6010019.Suche in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
