Beamforming Technique Assisted by Machine Learning Algorithm for Next Location Prediction

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.