Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction
International Journal of Wireless Communications and Mobile Computing
Volume 6, Issue 2, June 2018, Pages: 37-42
Received: Dec. 5, 2018;
Accepted: Jan. 2, 2019;
Published: Feb. 14, 2019
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Hussein Safwat Hasan Hasan, Department of Information and Communications Engineering, Myongji University, Yongin City, Republic of Korea
Humor Hwang, Department of Information and Communications Engineering, Myongji University, Yongin City, Republic of Korea
Technological improvement towards the development of location prediction advancement had attracted a great attention due to its broad application. Herein, intercalation of two widely scrutinized techniques were fused to form a synchronized location forecasting system. Using the underlying concept of beamforming (BF), an array of retro directive beams towards the phase sectioned field were emitted to determine the specific location of an entity or receiver. The receiver collects and sends back the data of beam emissions with respect to time and phase, machine learning (ML) technique were used to analyze the transcribed data to determine the phase with optimum beam reading that corresponds to the location of the receiver. Series of historical context will be analyzed by ML to predict the next location of the entity, emitting an array of signals pointing at the predicted location. Automatic location forecasting synchronization due to intricate systematic design were demonstrated. It should be noted that BF-ML technique collaboration for location prediction had never been reported before and driven by its advantages in wireless networking (such as elimination of interference and privacy issues) field of utilization can still be expanded.
Hussein Safwat Hasan Hasan,
Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction, International Journal of Wireless Communications and Mobile Computing.
Vol. 6, No. 2,
2018, pp. 37-42.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Gomes, João Bártolo, Clifton Phua, and Shonali Krishnaswamy. "Where will you go? mobile data mining for next place prediction." International Conference on Data Warehousing and Knowledge Discovery. Springer, Berlin, Heidelberg, 2013.
Xia, Linyuan, Qiumei Huang, and Dongjin Wu. "Decision Tree-Based Contextual Location Prediction from Mobile Device Logs." Mobile Information Systems 2018 (2018).
Barwise, Patrick, and Colin Strong. "Permission‐based mobile advertising." Journal of interactive Marketing 16.1 (2002): 14-24.
Anagnostopoulos, Theodoros, Christos Anagnostopoulos, and Stathes Hadjiefthymiades. "Mobility prediction based on machine learning." Mobile Data Management (MDM), 2011 12th IEEE International Conference on. Vol. 2. IEEE, 2011.
Jaiswal, Ayush, et al. "Location Prediction with Sparse GPS Data." Proceedings of the 8th International Conference on Geographic Information Science. 2014.
Anagnostopoulos, Theodoros, Christos Anagnostopoulos, and Stathes Hadjiefthymiades. "An adaptive machine learning algorithm for location prediction." International Journal of Wireless Information Networks 18.2 (2011): 88-99.
Wu, Ruizhi, et al. "Location prediction on trajectory data: A review." Big Data Mining and Analytics 1.2 (2018): 108-127.
Lu, Zhongqi, et al. "Next place prediction by learning with multiple models." Mobile Data Challenge Workshop. 2012.
Laurila, Juha K., et al. "The mobile data challenge: Big data for mobile computing research." Pervasive Computing. No. EPFL-CONF-192489. 2012.
Yavaş, Gökhan, et al. "A data mining approach for location prediction in mobile environments." Data & Knowledge Engineering 54.2 (2005): 121-146.
Palma, Andrey Tietbohl, et al. "A clustering-based approach for discovering interesting places in trajectories." Proceedings of the 2008 ACM symposium on Applied computing. ACM, 2008.
Kalousis, Alexandros. "Predicting the Location of Mobile Users: A Machine Learning Approach." (2009).
Schreiner, Clint. "Utilizing Digital Down Converter for Efficient Digital Beamforming." Red River Engineering.
Tall, Abdoulaye, Zwi Altman, and Eitan Altman. "Multilevel beamforming for high data rate communication in 5G networks." arXiv preprint arXiv: 1504.00280 (2015).
Darwish, Mohammad, and Cecil Lau. "A Software Radio Architecture for Smart Antennas." Spectrum Signal Processing, Inc., Vancouver, Canada (1998).
Shenghua, Zheng, Xu Dazhuan, and Jin Xueming. "A new receiver architecture for smart antenna with digital beamforming." Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2005. MAPE 2005. IEEE International Symposium on. Vol. 1. IEEE, 2005.