Enhancing Wave Propagation via Contextual Beamforming
Jaspreet Kaur, Qammer H. Abbasi, Abubakar Sharif, Olaoluwa Popoola, Muhammad Ali Imran, Hasan Tahir Abbas · 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI) · 2021
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.