  We present a neural network architecture for jointly learning user locations
and environment mapping up to isometry, in an unsupervised way, from channel
state information (CSI) values with no location information. The model is based
on an encoder-decoder architecture. The encoder network maps CSI values to the
user location. The decoder network models the physics of propagation by
parametrizing the environment using virtual anchors. It aims at reconstructing,
from the encoder output and virtual anchor location, the set of time of flights
(ToFs) that are extracted from CSI using super-resolution methods. The neural
network task is set prediction and is accordingly trained end-to-end. The
proposed model learns an interpretable latent, i.e., user location, by just
enforcing a physics-based decoder. It is shown that the proposed model achieves
sub-meter accuracy on synthetic ray tracing based datasets with single anchor
SISO setup while recovering the environment map up to 4cm median error in a 2D
environment and 15cm in a 3D environment
