The Seer

A 5G Subscriber Localization System

Presenter: Evan Peelen

Co-Presenter(s):
Victor Madrid, Tate Harsch-Hudspeth

Presenter Status: Undergraduate student

Academic Year: 20-21

Semester: Spring

Faculty Mentor: Mohamed Salem

Department: Engineering

Funding Source/Sponsor: Koret Scholars Program

President's Strategic Plan Goal: Sustainability and Environmental Inquiry

Screenshot URL: https://drive.google.com/uc?id=1amLPpOMnmcR3rh1tAvhJ8CrSUnBVjgZp

Abstract:
The Seer is a 5G subscriber localization system capable of predicting the direction-of-arrival (DOA) of low band 5G signals in anisotropic environments, as well as determining the location of 5G transmitters. The goal of The Seer lies in streamlining the process of location estimation to benefit telecommunications, assisted GPS (AGPS), and radio enthusiasts. By implementing a neural network that utilizes the received signal data from an array of antennas, The Seer can create an accurate and adaptably complex model of the environment it is trained in. The system is designed to be used in accrodance with Sub-6 5G NR frequencies within the 600 MHz (n5) to 850 MHz (n71) bands. Positioning the antennas a half-wavelength apart ensures that the main variation between antennas will be due to the antennas orientation relative to the transmitter (Tx). Analyzing the amplitude and phase of these received signals provides useful data for the creation of The Seer's deep learning model. Use of a neural network allows for a model to be created that matches the complexity of the urban indoor environments the team is targeting with our prototype. Other methods of pinpointing the location of an incoming signal use models that assume an isotropic environment, while true indoor urban environments are by no means isotropic. The existence of other EM waves, as well as multipath, and constructive and destructive interference add to the complexity of solving the inverse function of finding the direction using the received signal parameters. The Seer has the added benefit of learning and can be implemented in any environment through training. The Seer is capable of improving the current methods for determining the direction-of-arrival of low-band 5G signals, bolstering communication between a base station and transmitter, while lowering the economic impact of these large scale 5G systems.