Machine Learning Assisted Performance Analysis of RoFSO System with Different Modulation Techniques

Abstract

The rapid and continuous growth in demand for advanced communication system applications has led to the emergence of numerous new research directions aimed at addressing critical challenges related to speed, bandwidth efficiency, reliability, and security. In response to these demands, particular emphasis has been placed on the modernization of optical communication systems and the adaptation of modulation techniques to meet evolving requirements. This study investigates the fundamental modulation schemes commonly employed in optical communication systems, specifically quadrature amplitude modulation (QAM) and phase-shift keying (PSK). Using optical simulation software, a radio-over-free space optical (RoFSO) communication system is designed and its performance is analyzed. Furthermore, the simulation data were utilized in a Python™ environment to develop predictive models using several machine learning (ML) techniques: decision tree (DT), random forest (RF), support vector regression (SVR), multiple linear regression (MLR), and artificial neural networks (ANN). The performance of each ML model was assessed based on prediction accuracy. The main purpose of this paper is to develop a hybrid communication system that provides an efficient transmission environment by comparing different modulation techniques over the RoFSO link and also to build ML models that can predict system performance metrics with high accuracy using different ML methods. The results demonstrate that lower-order modulation schemes, such as 4-QAM and QPSK, yield superior performance compared to higher-order alternatives. Moreover, PSK-based modulation was found to outperform QAM in terms of overall system efficiency. Among the five ML models evaluated, prediction accuracies ranged from 74.9% to 93.2%, with an average accuracy of 84.5%. The random forest model achieved the highest performance, attaining a prediction accuracy of 93.2%.

Keywords

• Optical wireless communications (OWC)
• QAM
• PSK
• RoFSO
• Machine Learning

References

1. [1] Kishore, G. S., and Rallapalli, H., “Performance Assessment of M-ary ASK, FSK, PSK, QAM and FQAM in AWGN Channel,” 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 0273-0277, (2019). DOI: https://doi.org/10.1109/ICCSP.2019.8697922
2. [2] Shakthi Murugan, K. H., Sudharsanam, V., Padmavathi, B., Simon, J., Jacintha, V., Sumathi, K., “BER analysis of 40 Gbps Ro-FSO Communication System for 5 G applications under Fog Weather Conditions,” 2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Prayagraj, India, 1-6, (2020). DOI: https://doi.org/10.1109/UPCON50219.2020.9376490
3. [3] ITU, “IMT traffic estimates for the years 2020 to 2030, M Series, Report ITU-R M.2370-0”, [Online]. Available: https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2370-2015-PDF-E.pdf, (2015).
4. [4] Zhang, L., Xin L., Tang, Y., Xin, J., and Huang, S., “A survey on QoT prediction using machine learning in optical networks,” Optical Fiber Technology, 68: 102804, ISSN 1068-5200, (2022). DOI: https://doi.org/10.1016/j.yofte.2021.102804
5. [5] Aveta, F., Algedir, A., and Refai, H., “Quality of Transmission Estimation for Multi-User Free Space Optical Communication Using Supervised Machine Learning,” 2021 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW), Cleveland, OH, USA, 1-5, (2021). DOI: https://doi.org/10.1109/CCAAW50069.2021.9527304
6. [6] Kaur, S., Kaur, J., Sharma, A., “Predicting the performance of radio over free space optics system using machine learning techniques”, Optik, 281: 170798, (2023). DOI: https://doi.org/10.1016/j.ijleo.2023.170798
7. [7] Kaur, S., Lubana, A., “Performance investigation of Ro-FSO link under clear and fog conditions employing machine learning.” Journal of Optics, (2024). DOI: https://doi.org/10.1007/s12596-024-01904-z
8. [8] Kaur, S., Kumar Singh, S., and Sharma, A., “Machine learning based performance estimation of terrestrial Ro-FSO Link”. Physica Scripta, 98(7): 076001, (2023). DOI: https://dx.doi.org/10.1088/1402-4896/acde15

9. [9] Piyush J., Lakshmanan, M., Trivedi, S., Singh, T., and Jayanthi, N., “Bit Error Rate Analysis of K-PSK Modulation with OFDM RoFSO System over Double Generalized Gamma Turbulence Channel,” 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India, 578-581, (2020). DOI: https://doi.org/10.1109/ICACCCN51052.2020.9362874
10. [10] Mumtaz, A., Natali, Y., and Apriono, C., “Design of a WDM system with radio over free space optics for 5G fronthaul networks in an urban area,” International Conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023), Hybrid Conference, Iskandar Puteri, Malaysia, 7-11, (2023). DOI: https://doi.org/10.1049/icp.2023.1754
11. [11] Derouiche, S., Kameche, S., and Adardour, H. E., “Investigation and design of a MIMO ROFSO system using WDM for the purpose of 5G applications,” 2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC), Setif, Algeria, 1-4, (2024). DOI: https://doi.org/10.1109/ICEEAC61226.2024.10576366
12. [12] Tiwari, P., Rai, K., Shukla, N. K., Malik, N., Lakshmanan, M., and Rani, R., “Performance Enhancement of WDM-RoFSO Communication System under Different Weather Conditions,” 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), Vellore, India, 1-5, (2023). DOI: https://doi.org/10.1109/ViTECoN58111.2023.10157005
13. [13] Hamidnejad, E., Gholami, A., Zvanovec, S., and Ghassemlooy, Z., “Investigation of a WDM M-QAM RoF-RoFSO System,” 2020 3rd West Asian Symposium on Optical and Millimeter-wave Wireless Communication (WASOWC), Tehran, Iran, 1-5, (2020). DOI: https://doi.org/10.1109/WASOWC49739.2020.9409998
14. [14] Sarita, Sharma, N., and Agrawal, S., “Performance Improvement of Hybrid RoF and RoFSO System under High Atmospheric Turbulence,” 2024 11th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 160-167, (2024). DOI: https://doi.org/10.1109/SPIN60856.2024.10512344
15. [15] Sarita, Sharma, N., and Agrawal, S., “Performance Improvement of Hybrid RoF and RoFSO System using NRZI Encodingunder High Atmospheric Turbulence,” 2024 International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S), Una, India, 1-6, (2024). DOI: https://doi.org/10.1109/ICIC3S61846.2024.10602972
16. [16] Huang, H., Zeng, S., Li, L., Huang, Y., Zhang, Q., Zhang, J., Yao, J., “A Multi-Access RoFSO Communication System Incorporating a Microwave Photonic Channel Aggregator,” in Journal of Lightwave Technology, 43(5): 2184-2191, (2025). DOI: https://doi.org/10.1109/JLT.2024.3490673
17. [17] Rwaidi, A. M., Matarneh, A. M., and Alja'afreh, S. S., “The Performance of Millimeter-Wave Over FSO Communication Systems Under Adverse Atmospheric Conditions for 6G Applications,” 2023 14th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 1-6, (2023). DOI: https://doi.org/10.1109/ICICS60529.2023.10330513
18. [18] Derouiche, S., Kameche, S., and Adardour, H. E., “Evaluating the influence of diverse rain attenuation levels on 60GHz MMW transmission in free space optical communication systems,” 2024 International Conference on Advances in Electrical and Communication Technologies (ICAECOT), Setif, Algeria, 1-5, (2024). DOI: https://doi.org/10.1109/ICAECOT62402.2024.10827851
19. [19] Nistazakis, H. E., Stassinakis, A. N., Sandalidis, H. G., and Tombras, G. S., “QAM and PSK OFDM RoFSO Over M-Turbulence Induced Fading Channels,” in IEEE Photonics Journal, 7(1): 1-11, (2015). DOI: https://doi.org/10.1109/JPHOT.2014.2381670
20. [20] Song, S., Liu, X., Liu, Y., Wu, J., Wu, T., and Guo, L., “Implementation of Machine Learning-based Emergency Communication Using RoFSO-VLC/RF Convergence link,” 2022 Optical Fiber Communications Conference and Exhibition (OFC), San Diego, CA, USA, 1-3, (2022). DOI: https://doi.org/10.1364/OFC.2022.W3I.2
21. [21] Adnan, S. A., Ali, A. Mazin, Al-Saeedi, S., “Characteristics of RF signal in Free Space Optics(RoFSO) considering Rain Effect,” Journal of Engineering and Applied Sciences, 13(7): 1644-1648, (2018). URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2018.1644.1648
22. [22] Saleh, M. A., Abass, A. K., and Ali, M. H., “Enhancing performance of WDM-RoFSO communication system utilizing dual channel technique for 5G applications,”, Optical and Quantum Electronics, 54: 497, (2022). DOI: https://doi.org/10.1007/s11082-022-03857-8
23. [23] Balasaraswathi, M., Singh, M., Malhotra, J., Dhasarathan, V., “A high-speed radio-over-free-space optics link using wavelength division multiplexing-mode division multiplexing-multibeam technique,” Computers & Electrical Engineering, 87, (2020). DOI: https://doi.org/10.1016/j.compeleceng.2020.106779
24. [24] Roa, C. Á., Gültekin, Y. C., Wu, K., Korevaar, C. W., and Alvarado, A., “A Simplified FSO Channel Model with Weak Turbulence and Pointing Errors,” 2024 24th International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 1-6, (2024). DOI: https://doi.org/10.1109/ICTON62926.2024.10647275
25. [25] Rathi, D., Gajjar, A., and Joshi, H., “BER Performance Comparison of Gamma-Gamma FSO link for Different Modulations and Diversity Techniques,” 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Prayagraj, India, 1-5, (2022). DOI: https://doi.org/10.1109/UPCON56432.2022.9986410
26. [26] Petkovic, M., Cvetkovic, A., Narandzic, M., “Outage Probability Analysis of RF/FSO-VLC Communication Relaying System,” 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), 1-5, (2018). DOI: https://doi.org/10.1109/CSNDSP.2018.8471801
27. [27] Suhartomo, A., and Vincent, V., “Comparison of BER Performance for M-ary QAM and PSK on DWT-based OFDM System with PTS Technique Through AWGN Channel,” 2020 IEEE International Conference on Sustainable Engineering and Creative Computing (ICSECC), Indonesia, 5-10, (2020). DOI: https://doi.org/10.1109/ICSECC51444.2020.9557556
28. [28] Apu, R. S., Ahmed, M. S., Raian, M. H., and Matin, M. A., “Performance Evaluation of Different Types of modulation in Optical Communication,” 2021 2nd International Conference for Emerging Technology (INCET), Belagavi, India, 1-4, (2021). DOI: https://doi.org/10.1109/INCET51464.2021.9456046
29. [29] Rappaport, T., Wireless Communications: Principles and Practice. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2nd edition, (2001).
30. [30] Ab Aziz, S. H., Mohd Nor, N. A., Abidin, M. S. Z., “Performance Analysis of a Hybrid Free Space Optics/ Visible Light Communication Systems,” Asian Journal of Electrical and Electronic Engineering, 2(2): (2022). DOI: https://doi.org/10.69955/ajoeee.2022.v2i2.35
31. [31] Paudyal, P., Shen, S., Yan, S., and Simeonidou, D., “Toward Deployments of ML Applications in Optical Networks,” in IEEE Photonics Technology Letters, 33(11): 537-540, (2021). DOI: https://doi.org/10.1109/LPT.2021.3074586
32. [32] Amirabadi, M. A., “A Survey on Machine Learning for Optical Communication [Machine Learning View]”, arXiv:1909.05148, (2019). DOI: https://doi.org/10.48550/arXiv.1909.05148
33. [33] Esmail. M. A., “Optical Wireless Performance Monitoring Using Asynchronous Amplitude Histograms,” in IEEE Photonics Journal, 13(3): 1-9, (2021). DOI: https://doi.org/10.1109/JPHOT.2021.3080593
34. [34] Esmail, M., Saif, W., Ragheb, A., and Alshebeili, S., “Free space optic channel monitoring using machine learning,” Optics Express, 29(7): 10967-10981, (2021). DOI: https://doi.org/10.1364/OE.416777