Throughput optimization of optical-NOMA system using ML algorithms for diverse order QAM transmission schemes
-
Rajneesh Pareek
Abstract
The paper offers a design of an optical non-orthogonal multiple access (O-NOMA) system with Rayleigh fading channels by analysing the bit error rate (BER) and power spectral density (PSD) using machine learning (ML)-based and traditional signal detection methods. The primary focus is on various QAM modulation orders, such as 32-QAM, 64-QAM, 256-QAM, and 512-QAM. The research compares the performance of different detectors including deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), and conventional techniques. Among these, the DNN always yields better BER performance up to 10−5 BER at SNR values 12 dB lower than conventional techniques. For 32-QAM, in particular, the DNN provides a gain of 10.3 dB compared to conventional techniques, while similar gains are realized for higher QAM orders. PSD analysis also shows that the DNN is compact in terms of its spectral footprint, achieving out-of-band emissions as low as −690 dB, essential for reducing interference and maximizing spectral efficiency. Such results highlight the promise of using ML-based detection – particularly DNN – as an efficient and effective method for future, high-capacity optical NOMA systems functioning over a wide range of modulation schemes.
-
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, Gaur, N, Gupta, M, Nanthaamornphong, A. Implementation of the deep learning method for signal detection in massive-MIMO-NOMA systems. Heliyon 2024;10. https://doi.org/10.1016/j.heliyon.2024.e25374.Search in Google Scholar PubMed PubMed Central
2. Cheng, Q, Shi, Z, Yuan, J, Lin, H. MIMO-ODDM signal detection: a spatial-based generative adversarial network approach. IEEE Trans Wireless Commun 2024;23:12499–512. https://doi.org/10.1109/TWC.2024.3392881.Search in Google Scholar
3. 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.Search in Google Scholar
4. Huang, X, Yuan, Y, Li, J. MIMO signal detection based on IM-LSTMNet model. Electronics 2024;13:3153. https://doi.org/10.3390/electronics13163153.Search in Google Scholar
5. Jiang, X, Zhang, L. SD-based low-complexity signal detection algorithm in massive MIMO systems. Mobile Network Appl 2024;29:1203–11. https://doi.org/10.1007/s11036-022-02085-4.Search in Google Scholar
6. Kumar, A, Albreem, MA, Gupta, M, Alsharif, MH, Kim, S. Future 5G network based smart hospitals: hybrid detection technique for latency improvement. IEEE Access 2020;8:153240–9. https://doi.org/10.1109/ACCESS.2020.3017625.Search in Google Scholar
7. Kumar, A, Gaur, N, Nanthaamornphong, A. Machine learning RNNs, SVM and NN algorithm for massive-MIMO-OTFS 6G waveform with Rician and Rayleigh channel. Egyptian Inf J 2024;27. https://doi.org/10.1016/j.eij.2024.100531.Search in Google Scholar
8. Ro, J, Ha, J, Lee, W, You, Y, Song, H. Improved MIMO signal detection based on DNN in MIMO-OFDM system. Comput Mater Continua (CMC) 2022;70:3625–36. https://doi.org/10.32604/cmc.2022.020596.Search in Google Scholar
9. Huang, X, Yuan, Y, Li, J. MIMO signal detection based on IM-LSTMNet model. Electronics 2024;13:1–18. https://doi.org/10.3390/electronics13163153.Search in Google Scholar
10. Omondi, G, Olwal, TO. Variational autoencoder-enhanced deep neural network-based detection for MIMO systems. e-Prime - Adv Electr Eng Electron Energy 2023;6:100335. https://doi.org/10.1016/j.prime.2023.100335.Search in Google Scholar
11. Islam, MM, Islam, MA, Ahmed, MF. A DNN-based 5G MIMO system adopting a mix of tactics. Discov Electron 2025;2:1–16. https://doi.org/10.1007/s44291-025-00055-0.Search in Google Scholar
12. Guo, J, Zhao, Q, Guo, L, Guo, S, Liang, G. An improved signal detection algorithm for a mining-purposed MIMO-OFDM IoT-based system. Electron Res Arch 2023;31:3943–62.10.3934/era.2023200Search in Google Scholar
13. Al-Makhlasawy, RM, Khairy, M, El-Shafai, W. Recurrent neural networks for enhanced joint channel estimation and interference cancellation in FBMC and OFDM systems: unveiling the potential for 5G networks. EURASIP J Appl Signal Process 2023;2023:1–32. https://doi.org/10.1186/s13634-023-01077-0.Search in Google Scholar
14. Victor, CM, Mvuma, AN, Mrutu, SI. Multi-input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO. IET Commun 2023:1899–906. https://doi.org/10.1049/cmu2.12670.Search in Google Scholar
15. Ratnam, DV, Rao, KN. Bi-LSTM based deep learning method for 5G signal detection and channel estimation. AIMS Electron Electr Eng 2021:334–41. https://doi.org/10.3934/electreng.2021017.Search in Google Scholar
16. Yao, H, Li, T, Song, Y, Ji, W, Liang, Y, Li, F, et al.. Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems. EURASIP J Appl Signal Process 2022;2022:1–38. https://doi.org/10.1186/s13634-022-00885-0.Search in Google Scholar
17. Kumar, A, Gour, N, Sharma, H, Shorfuzzaman, M, Masud, M. Hybrid detection techniques for 5G and B5G M-MIMO system. Alex Eng J 2023;75:429–37. https://doi.org/10.1016/j.aej.2023.06.005.Search in Google Scholar
18. Jiang, X, Zhang, L. SD-based low-complexity signal detection algorithm in massive MIMO systems. Mobile Network Appl 2024:1203–11. https://doi.org/10.1007/s11036-022-02085-4.Search in Google Scholar
19. 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.Search in Google Scholar PubMed PubMed Central
20. 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.Search in Google Scholar
21. Tsipi, L, Karavolos, M, Papaioannou, G, Volakaki, M, Vouyioukas, D. Machine learning-based methods for MCS prediction in 5G networks. Telecommun Syst 2024;86:705–28. https://doi.org/10.1007/s11235-024-01158-x.Search in Google Scholar
22. Panda, B, Senanayake, DD, Singh, P. A machine learning approach with decision tree-based signal detection for MIMO-NOMA systems. In: 2024 3rd International conference on artificial intelligence for internet of things (AIIoT). Vellore, India: IEEE; 2024:1–6 pp.10.1109/AIIoT58432.2024.10574572Search in Google Scholar
23. Kumar, A, Gaur, N, Aziz, N. A SIC and ML approach for MIMO non-orthogonal multiple access signal detection. Results in Optics 2025;18. https://doi.org/10.1016/j.rio.2025.100792.Search in Google Scholar
24. Kumar, A, Gaur, N, Nanthaamornphong, A. Improving the latency for 5G/B5G based smart healthcare connectivity in rural area. Sci Rep 2024;14:6976. https://doi.org/10.1038/s41598-024-57641-7.Search in Google Scholar PubMed PubMed Central
25. Chataut, R, Akl, R. Massive MIMO systems for 5G and beyond networks – overview, recent trends, challenges, and future research direction. Sensors 2020;20:2753. https://doi.org/10.3390/s20102753.Search in Google Scholar PubMed PubMed Central
© 2025 Walter de Gruyter GmbH, Berlin/Boston
