volume | PIER Journals

Abstract

This paper presents the application of machine learning-based approach toward prediction of path loss for the large intelligent surface-assisted wireless communication in smart radio environment. Two bagging ensemble methods, namely K-nearest neighbor and random forest, are exploited to build the path loss prediction models by using the training dataset. To generate the data samples without having to run measurement campaign, a path loss model is developed owning to the similarity between the large intelligent surface-assisted wireless communication and the reflector antenna system. Simple path loss expression is deduced from the system gain of the reflector antenna system, and it is used to generate the data samples. Simulation results are presented to verify the prediction accuracy of the path loss predictions models. The prediction performances of the trained path loss models are assessed based on the complexity and accuracy metrics, including R² score, mean absolute error, and root mean square error. It is demonstrated that the machine learning-based models can provide high prediction accuracy and acceptable complexity. The K-nearest neighbor algorithm outperforms random forest algorithm, and it has smaller prediction errors.

1. Yuan, J., H. Q. Ngo, and M. Matthaiou, "Towards large intelligent surface (LIS)-based communications," IEEE Transactions on Communications, Vol. 68, No. 10, 6568-6582, 2020.

2. Najafi, M., V. Jamali, R. Schober, and H. V. Poor, "Physics-based modeling and scalable optimization of large intelligent reflecting surfaces," IEEE Transactions on Communications, Vol. 69, No. 4, 2673-2691, 2021.

3. Dardari, D., "Communicating with large intelligent surfaces: Fundamental limits and models," IEEE Journal on Selected Areas in Communications, Vol. 38, No. 11, 2526-2537, 2020.

4. Han, Y., W. Tang, S. Jin, C.-K. Wen, and X. Ma, "Large intelligent surface-assisted wireless communication exploiting statistical CSI," IEEE Transactions on Vehicular Technology, Vol. 68, No. 8, 8238-8242, 2019.

5. Kundu, N. K. and M. R. Mckay, "Large intelligent surfaces with channel estimation overhead: Achievable rate and optimal configuration," IEEE Wireless Communications Letters, Vol. 10, No. 5, 986-990, 2021.

6. Taha, A., M. Alrabeiah, and A. Alkhateeb, "Deep learning for large intelligent surfaces in millimeter wave and massive MIMO systems," 2019 IEEE Global Communications Conference (GLOBECOM), 1-6, 2019.

7. Di Renzo, M., F. Habibi Danufane, X. Xi, J. de Rosny, and S. Tretyakov, "Analytical modeling of the path-loss for reconfigurable intelligent surfaces - Anomalous mirror or scatterer?," 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 1-5, 2020.

8. Tang, W., M. Z. Chen, X. Chen, J. Y. Dai, Y. Han, M. Di Renzo, Y. Zeng, S. Jin, Q. Cheng, and T. J. Cui, "Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement," IEEE Transactions on Wireless Communications, Vol. 20, No. 1, 421-439, 2021.

9. Yildirim, I., A. Uyrus, and E. Basar, "Modeling and analysis of reconfigurable intelligent surfaces for indoor and outdoor applications in future wireless networks," IEEE Transactions on Communications, Vol. 69, No. 2, 1290-1301, 2021.

10. Wen, J., Y. Zhang, G. Yang, Z. He, and W. Zhang, "Path loss prediction based on machine learning methods for aircraft cabin environments," IEEE Access, Vol. 7, 159251-159261, 2019.

11. Zhang, Y., J. Wen, G. Yang, Z. He, and J. Wang, "Path loss prediction based on machine learning: Principle, method, and data expansion," Applied Sciences, Vol. 9, No. 9, 2019, [Online]. Available: https://www.mdpi.com/2076-3417/9/9/1908.

12. Duangsuwan, S., P. Juengkittikul, and M. Myint Maw, "Path loss characterization using machine learning models for GS-to-UAV-enabled communication in smart farming scenarios," International Journal of Antennas and Propagation, Vol. 2021, 5524709, Aug. 2021, [Online]. Available: https://doi.org/10.1155/2021/5524709.

13. Aldossari, S. and K.-C. Chen, "Predicting the path loss of wireless channel models using machine learning techniques in mmWave urban communications," 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC), 1-6, 2019.

14. Juang, R.-T., "Explainable deep-learning-based path loss prediction from path profiles in urban environments," Applied Sciences, Vol. 11, No. 15, 2021, [Online]. Available: https://www.mdpi.com/2076-3417/11/15/6690.

15. Zhang, Y., J. Wen, G. Yang, Z. He, and X. Luo, "Air-toair path loss prediction based on machine learning methods in urban environments," Wireless Communications and Mobile Computing, Vol. 2018, 8489326, Jun. 2018, [Online]. Available: https://doi.org/10.1155/2018/8489326.

16. Ellingson, S. W., "Path loss in reconfigurable intelligent surface-enabled channels," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 829-835, IEEE, 2021.

17. Molisch, A. F., K. Balakrishnan, D. Cassioli, C.-C. Chong, S. Emami, A. Fort, Johan, Karedal, J. Kunisch, H. G. Schantz, U. G. Schuster, and K. Siwiak, "IEEE 802.15.4a channel model-final report,", 2004.

18. Hu, S., F. Rusek, and O. Edfors, "Beyond massive MIMO: The potential of data transmission with large intelligent surfaces," IEEE Transactions on Signal Processing, Vol. 66, No. 10, 2746-2758, 2018.

19. Jide, Y., H.-Q. Ngo, and M. Matthaiou, "Large intelligent surface (LIS)-based communications: New features and system layouts," IEEE International Conference on Communications, IEEE, arXiv:2002.12183, Feb. 2020.

20. Balanis, C. A., Antenna Theory: Analysis and Design, Wiley-Interscience, 2005.

21. Greenquist, R. E. and A. J. Orlando, "An analysis of passive reflector antenna systems," Proceedings of the IRE, Vol. 42, No. 7, 1173-1178, 1954.

22. Silver, S., Microwave Antenna Theory and Design, S. Silver, et al., Ed., [Massachusetts Institute of Technology. Radiation Laboratory Series. No. 12], 1949, [Online]. Available: https://books.google.com.eg/books?id=Fi42MwEACAAJ.

23. MATLAB "Version 7.10.0 (R2010a),", Natick, The MathWorks Inc., Massachusetts, 2010.

24. Ozdogan, O., E. Bjornson, and E. G. Larsson, "Intelligent reflecting surfaces: Physics, propagation, and pathloss modeling," IEEE Wireless Communications Letters, Vol. 9, No. 5, 581-585, 2020.

25. Muller, A. and S. Guido, "Introduction to machine learning with python: A guide for data scientists,", O'Reilly Media, 2016, [Online]. Available: https://books.google.com.eg/books?id=vbQlDQAAQBAJ.

26. Huang, J., N. Huang, L. Zhang, and H. Xu, "A method for feature selection based on the correlation analysis," Proceedings of 2012 International Conference on Measurement, Information and Control, Vol. 1, 529-532, 2012.

27. Mohammed, R., J. Rawashdeh, and M. Abdullah, "Machine learning with oversampling and undersampling techniques: Overview study and experimental results," 2020 11th International Conference on Information and Communication Systems (ICICS), 243-248, 2020.

28. Ray, S., "A quick review of machine learning algorithms," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 35-39, 2019.

29. Popoola, S. I., A. Jefia, A. A. Atayero, O. Kingsley, N. Faruk, O. F. Oseni, and R. O. Abolade, "Determination of neural network parameters for path loss prediction in very high frequency wireless channel," IEEE Access, Vol. 7, 150 462-150 483, 2019.

30. Breiman, L., "Random forests," Machine Learning, Vol. 45, No. 1, 5-32, Oct. 2001, [Online]. Available: https://doi.org/10.1023/A:1010933404324.

31. Breiman, L., "Bagging predictors," Machine Learning, Vol. 24, No. 2, 123-140, Aug. 1996, [Online]. Available: https://doi.org/10.1007/BF00058655.

32. Rahim, A., Y. Rasheed, F. Azam, M. W. Anwar, M. A. Rahim, and A. W. Muzaffar, "An integrated machine learning framework for effective prediction of cardiovascular diseases," IEEE Access, Vol. 9, 106 575-106 588, 2021.

33. Buitinck, L., G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, and G. Varoquaux, "API design for machine learning software: Experiences from the scikit-learn project," ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 108-122, 2013.

34. Lin, W., Z. Wu, L. Lin, A. Wen, and J. Li, "An ensemble random forest algorithm for insurance big data analysis," IEEE Access, Vol. 5, 16 568-16 575, 2017.

35. Matsushita, Y. and T. Wada, "Principal component hashing: An accelerated approximate nearest neighbor search," Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology, Springer-Verlag, Heidelberg, Berlin, 2009, [Online]. Available: https://doi.org/10.1007/978-3-540-92957-4_33.

36. Jouppi, N. P., C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P.-L. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, C. R. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, D. Killebrew, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, M. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and D. H. Yoon, "In-datacenter performance analysis of a tensor processing unit," 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA), 1-12, 2017.

37. "Anaconda software distribution,", 2020, [Online]. Available: https://docs.anaconda.com/.

Dr. Wael Elshennawy