VIn-Loc: Towards Deep Learning-Based Environment Agnostic Localization

Rushabha Balaji, Aditya Sant, Akshay Malhotra, Keya Patani, Shahab Hamidi-Rad

Keywords: Localization, Deep Learning, RF Fingerprinting, Environment-Agnostic, View-Agnostic

TL;DR: Paper proposes a transformer-based model for user localization in a given environment; the model is invariant to the number of transmitters as well as the configuration of the transmitters.

Abstract:

User localization in wireless sensing and communication systems
are essential for ensuring improved quality of service.
To this end, the field of radio-frequency (RF) fingerprinting,
extracting user location information from multiple transmit
receive points (TRPs) using the wireless channel information
between the user and different TRP, has been heavily studied
and developed, both through model-based and deep neural
network (DNN)-aided methods. However, conventional RF
fingerprinting approaches face two major limitations (i) Dependence
on fixed environment and TRP locations, and (ii)
Relying on inputs from a fixed number of TRPs. Addressing
these limitations, this work introduces the view independent
localization (VIn-Loc) model - A transformer-based DNN
framework that localizes users invariant to the number and location
of the TRPs. This work presents the first step towards
environment-agnostic user localization using both line-ofsight
(LoS) and non-LoS channel information. This approach
is rigorously validated on different statistical and ray tracing
models, from wireless channels with only NLoS paths, to outdoor
city blocks with both LOS and NLoS paths. Experimental
results highlight the strength of the proposed model, VIn-
Loc, over DNN-based RF fingerprinting.

Submission Number: 16