@inproceedings{6ce93c36b505457cb163251d3d447757,
title = "Enhancing Wave Propagation via Contextual Beamforming",
abstract = "Beamforming is a cellular base station traffic-signaling system that determines the most effective data-delivery path for a specific user while reducing interference for neighboring users, and it has now become an integral technology in modern wireless communication systems. Contextual beamforming has evolved as an intelligent method in which the location information of a mobile user is predicted based on prior knowledge, and subsequently reconfigure the antenna arrays. Such a system relies heavily on a machine and deep learning applied on the data gathered to infer the context. This paper presents an antenna beamforming approach that exploits the location information of a moving mobile user in a mobile network. Our results show that network parameters such as the received power and propagation path information can assist to construct a machine learning-based adaptive beamforming framework.",
keywords = "Antenna Array Beamforming, Contextual Beamforming, Machine Learning, Massive MIMO",
author = "Jaspreet Kaur and Abbasi, \{Qammer H.\} and Sharif, \{Abu Bakar\} and Olaoluwa Popoola and Imran, \{Muhammad Ali\} and Abbas, \{Hasan T.\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 ; Conference date: 04-12-2021 Through 10-12-2021",
year = "2021",
doi = "10.1109/APS/URSI47566.2021.9703804",
language = "English",
series = "2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "781--782",
booktitle = "2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings",
address = "United States",
}