Three-Way Deep Neural Network for Radio Frequency Map Generation and
Source Localization
- URL: http://arxiv.org/abs/2111.12175v1
- Date: Tue, 23 Nov 2021 22:25:10 GMT
- Title: Three-Way Deep Neural Network for Radio Frequency Map Generation and
Source Localization
- Authors: Kuldeep S. Gill, Son Nguyen, Myo M. Thein, Alexander M. Wyglinski
- Abstract summary: Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies.
In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
- Score: 67.93423427193055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we present a Generative Adversarial Network (GAN) machine
learning model to interpolate irregularly distributed measurements across the
spatial domain to construct a smooth radio frequency map (RFMap) and then
perform localization using a deep neural network. Monitoring wireless spectrum
over spatial, temporal, and frequency domains will become a critical feature in
facilitating dynamic spectrum access (DSA) in beyond-5G and 6G communication
technologies. Localization, wireless signal detection, and spectrum
policy-making are several of the applications where distributed spectrum
sensing will play a significant role. Detection and positioning of wireless
emitters is a very challenging task in a large spectral and spatial area. In
order to construct a smooth RFMap database, a large number of measurements are
required which can be very expensive and time consuming. One approach to help
realize these systems is to collect finite localized measurements across a
given area and then interpolate the measurement values to construct the
database. Current methods in the literature employ channel modeling to
construct the radio frequency map, which lacks the granularity for accurate
localization whereas our proposed approach reconstructs a new generalized
RFMap. Localization results are presented and compared with conventional
channel models.
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