Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction

Hussein Safwat Hasan Hasan; Humor Hwang

doi:doi:10.11648/j.wcmc.20180602.11

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Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction

Hussein Safwat Hasan Hasan, Humor Hwang

Published in International Journal of Wireless Communications and Mobile Computing (Volume 6, Issue 2)

Received: 5 December 2018 Accepted: 2 January 2019 Published: 14 February 2019

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Abstract

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.

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 6, Issue 2)
DOI	10.11648/j.wcmc.20180602.11
Page(s)	37-42
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning (ML), Beamforming (BF), Scans, Phase Delay, Tracking Algorithm

References

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[2]	Xia, Linyuan, Qiumei Huang, and Dongjin Wu. "Decision Tree-Based Contextual Location Prediction from Mobile Device Logs." Mobile Information Systems 2018 (2018).
[3]	Barwise, Patrick, and Colin Strong. "Permission‐based mobile advertising." Journal of interactive Marketing 16.1 (2002): 14-24.
[4]	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.
[5]	Jaiswal, Ayush, et al. "Location Prediction with Sparse GPS Data." Proceedings of the 8th International Conference on Geographic Information Science. 2014.
[6]	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.
[7]	Wu, Ruizhi, et al. "Location prediction on trajectory data: A review." Big Data Mining and Analytics 1.2 (2018): 108-127.
[8]	Lu, Zhongqi, et al. "Next place prediction by learning with multiple models." Mobile Data Challenge Workshop. 2012.
[9]	Laurila, Juha K., et al. "The mobile data challenge: Big data for mobile computing research." Pervasive Computing. No. EPFL-CONF-192489. 2012.
[10]	Yavaş, Gökhan, et al. "A data mining approach for location prediction in mobile environments." Data & Knowledge Engineering 54.2 (2005): 121-146.
[11]	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.
[12]	Kalousis, Alexandros. "Predicting the Location of Mobile Users: A Machine Learning Approach." (2009).
[13]	Schreiner, Clint. "Utilizing Digital Down Converter for Efficient Digital Beamforming." Red River Engineering.
[14]	Tall, Abdoulaye, Zwi Altman, and Eitan Altman. "Multilevel beamforming for high data rate communication in 5G networks." arXiv preprint arXiv: 1504.00280 (2015).
[15]	Darwish, Mohammad, and Cecil Lau. "A Software Radio Architecture for Smart Antennas." Spectrum Signal Processing, Inc., Vancouver, Canada (1998).
[16]	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.

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APA Style

Hussein Safwat Hasan Hasan, Humor Hwang. (2019). Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction. International Journal of Wireless Communications and Mobile Computing, 6(2), 37-42. https://doi.org/10.11648/j.wcmc.20180602.11

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ACS Style

Hussein Safwat Hasan Hasan; Humor Hwang. Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction. Int. J. Wirel. Commun. Mobile Comput. 2019, 6(2), 37-42. doi: 10.11648/j.wcmc.20180602.11

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AMA Style

Hussein Safwat Hasan Hasan, Humor Hwang. Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction. Int J Wirel Commun Mobile Comput. 2019;6(2):37-42. doi: 10.11648/j.wcmc.20180602.11

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@article{10.11648/j.wcmc.20180602.11,
  author = {Hussein Safwat Hasan Hasan and Humor Hwang},
  title = {Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {6},
  number = {2},
  pages = {37-42},
  doi = {10.11648/j.wcmc.20180602.11},
  url = {https://doi.org/10.11648/j.wcmc.20180602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20180602.11},
  abstract = {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.},
 year = {2019}
}

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TY  - JOUR
T1  - Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction
AU  - Hussein Safwat Hasan Hasan
AU  - Humor Hwang
Y1  - 2019/02/14
PY  - 2019
N1  - https://doi.org/10.11648/j.wcmc.20180602.11
DO  - 10.11648/j.wcmc.20180602.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 37
EP  - 42
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20180602.11
AB  - 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.
VL  - 6
IS  - 2
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Hussein Safwat Hasan Hasan, Humor Hwang. (2019). Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction. International Journal of Wireless Communications and Mobile Computing, 6(2), 37-42. https://doi.org/10.11648/j.wcmc.20180602.11

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ACS Style

Hussein Safwat Hasan Hasan; Humor Hwang. Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction. Int. J. Wirel. Commun. Mobile Comput. 2019, 6(2), 37-42. doi: 10.11648/j.wcmc.20180602.11

Copy | Download

AMA Style

Hussein Safwat Hasan Hasan, Humor Hwang. Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction. Int J Wirel Commun Mobile Comput. 2019;6(2):37-42. doi: 10.11648/j.wcmc.20180602.11

Copy | Download

@article{10.11648/j.wcmc.20180602.11,
  author = {Hussein Safwat Hasan Hasan and Humor Hwang},
  title = {Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {6},
  number = {2},
  pages = {37-42},
  doi = {10.11648/j.wcmc.20180602.11},
  url = {https://doi.org/10.11648/j.wcmc.20180602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20180602.11},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction
AU  - Hussein Safwat Hasan Hasan
AU  - Humor Hwang
Y1  - 2019/02/14
PY  - 2019
N1  - https://doi.org/10.11648/j.wcmc.20180602.11
DO  - 10.11648/j.wcmc.20180602.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 37
EP  - 42
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20180602.11
AB  - 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.
VL  - 6
IS  - 2
ER  -

Copy | Download