##plugins.themes.academic_pro.article.sidebar##

Download
pdf
Statistic
Read Counter : 89 Download : 71

##plugins.themes.academic_pro.article.main##

Abstract

This article's main goal is to carry out an in-depth analysis of the free space optic channel's performance. The ultimate goal is to address the various obstacles associated with terrestrial infrastructure and channel leasing. In this article the four channels provided by the wavelength-division multiplexing (WDM) transmitter and transmitted to the Free-space optical communication (FSO) transceiver, resulting in successful reception of output signals at a speed of 10 Gbps, with a free channel range of 200m. The best iteration achieved an attenuation of 25dB/km. The optisystems software simulation package conducted 10 iterations, and the average total power was measured to be -34.22 dBm, while the maximum average Q-factor was 114.43. These results are considered satisfactory for the entire project, as they indicate low data error rates. (9 pt) .

Keywords

Free space fiber optics communication systems

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Zainab Mohammed Abdulkareem, Yaser Issam Hamodi, Sarmad Sami M.Ali, & Saif Mohamed Baraa Al-Sabit. (2023). An Analysis of Performance in Free-Space Optical Communication Using Eight Wavelength Division Multiplexing. Texas Journal of Engineering and Technology, 24, 13–19. Retrieved from https://zienjournals.com/index.php/tjet/article/view/4400

References

  1. M. Sigala, A. Beer, L. Hodgson, and A. O’Connor, Big Data for Measuring the Impact of Tourism Economic Development Programmes: A Process and Quality Criteria Framework for Using Big Data. 2019.
  2. G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.
  3. C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
  4. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.
  5. K. Sivaraman, R. M. V. Krishnan, B. Sundarraj, and S. Sri Gowthem, “Network failure detection and diagnosis by analyzing syslog and SNS data: Applying big data analysis to network operations,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9 Special Issue 3, pp. 883–887, 2019, doi: 10.35940/ijitee.I3187.0789S319.
  6. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, “A decentralized privacy-preserving healthcare blockchain for IoT,” Sensors (Switzerland), vol. 19, no. 2, pp. 1–17, 2019, doi: 10.3390/s19020326.
  7. F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, “Quantifying uncertainty in internet of medical things and big-data services using intelligence and deep learning,” IEEE Access, vol. 7, pp. 115749–115759, 2019, doi: 10.1109/ACCESS.2019.2931637.
  8. Kumar S, Singh M. Big data analytics for healthcare industry: impact, applications, and tools. Big data mining and analytics. 2018 Oct 14;2(1):48-57.
  9. Ang LM, Seng KP, Ijemaru GK, Zungeru AM. Deployment of IoV for smart cities: Applications, architecture, and challenges. IEEE access. 2018 Dec 16;7:6473-92.
  10. Lau BP, Marakkalage SH, Zhou Y, Hassan NU, Yuen C, Zhang M, Tan UX. A survey of data fusion in smart city applications. Information Fusion. 2019 Dec 1;52:357-74.
  11. Wu Y, Chen Y, Wang L, Ye Y, Liu Z, Guo Y, Fu Y. Large scale incremental learning. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition 2019 (pp. 374-382).
  12. Mosavi A, Shamshirband S, Salwana E, Chau KW, Tah JH. Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Engineering Applications of Computational Fluid Mechanics. 2019 Jan 1;13(1):482-92.
  13. Palanisamy V, Thirunavukarasu R. Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud University-Computer and Information Sciences. 2019 Oct 1;31(4):415-25.
  14. .J. Sadowski, “When data is capital: Datafication, accumulation, and extraction,” Big Data Soc., vol. 6, no. 1, pp. 1–12, 2019, doi: 10.1177/2053951718820549.
  15. .J. R. Saura, B. R. Herraez, and A. Reyes-Menendez, “Comparing a traditional approach for financial brand communication analysis with a big data analytics technique,” IEEE Access, vol. 7, pp. 37100–37108, 2019, doi: 10.1109/ACCESS.2019.2905301.
  16. D. Nallaperuma et al., “Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4679–4690, 2019, doi: 10.1109/TITS.2019.2924883.
  17. S. Schulz, M. Becker, M. R. Groseclose, S. Schadt, and C. Hopf, “Advanced MALDI mass spectrometry imaging in pharmaceutical research and drug development,” Curr. Opin. Biotechnol., vol. 55, pp. 51–59, 2019, doi: 10.1016/j.copbio.2018.08.003.
  18. C. Shang and F. You, “Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era,” Engineering, vol. 5, no. 6, pp. 1010–1016, 2019, doi: 10.1016/j.eng.2019.01.019.
  19. Y. Yu, M. Li, L. Liu, Y. Li, and J. Wang, “Clinical big data and deep learning: Applications, challenges, and future outlooks,” Big Data Min. Anal., vol. 2, no. 4, pp. 288–305, 2019, doi: 10.26599/BDMA.2019.9020007.
  20. M. Huang, W. Liu, T. Wang, H. Song, X. Li, and A. Liu, “A queuing delay utilization scheme for on-path service aggregation in services-oriented computing networks,” IEEE Access, vol. 7, pp. 23816–23833, 2019, doi: 10.1109/ACCESS.2019.2899402.
  21. G. Xu, Y. Shi, X. Sun, and W. Shen, “Internet of things in marine environment monitoring: A review,” Sensors (Switzerland), vol. 19, no. 7, pp. 1–21, 2019.
  22. M. Aqib, R. Mehmood, A. Alzahrani, I. Katib, A. Albeshri, and S. M. Altowaijri, Smarter traffic prediction using big data, in-memory computing, deep learning and gpus, vol. 19, no. 9. 2019.
  23. S. Leonelli and N. Tempini, Data Journeys in the Sciences. 2020.
  24. N. Stylos and J. Zwiegelaar, Big Data as a Game Changer: How Does It Shape Business Intelligence Within a Tourism and Hospitality Industry Context? 2019.
  25. Song Q, Ge H, Caverlee J, Hu X. Tensor completion algorithms in big data analytics. ACM Transactions on Knowledge Discovery from Data (TKDD). 2019 Jan 9;13(1):1-48.