  This paper presents a new map-assisted localization approach utilizing Chanel
State Information (CSI) in Massive Multiple-Input Multiple-Output (MIMO)
systems. Map-assisted localization is an environment-aware approach in which
the communication system has information regarding the surrounding environment.
By combining radio frequency ray tracing parameters of the multipath components
(MPC) with the environment map, it is possible to accomplish localization.
Unfortunately, in real-world scenarios, ray tracing parameters are typically
not explicitly available. Thus, additional complexity is added at a base
station to obtain this information. On the other hand, CSI is a common
communication parameter, usually estimated for any communication channel. In
this work, we leverage the already available CSI data to propose a novel
map-assisted CSI localization approach, referred to as MAP-CSI. We show that
Angle-of-Departure (AoD) and Time-of-Arrival (ToA) can be extracted from CSI
and then be used in combination with the environment map to localize the user.
We perform simulations on a public MIMO dataset and show that our method works
for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We compare
our method to the state-of-the-art (SoA) method that uses the ray tracing data.
Using MAP-CSI, we accomplish an average localization error of 1.8 m in LOS and
2.8 m in mixed (combination of LOS and NLOS samples) scenarios. On the other
hand, SoA ray tracing has an average error of 1.0 m and 2.2 m, respectively,
but requires explicit AoD and ToA information to perform the localization task.
