Towards Deep Learning-Based Environment Agnostic Localization

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.