  Artificial Intelligence (AI) solutions for wireless location estimation are
likely to prevail in many real-world scenarios. In this work, we demonstrate
for the first time how the Cramer-Rao upper bound on localization accuracy can
facilitate efficient neural-network solutions for wireless location estimation.
In particular, we demonstrate how the number of neurons for the network can be
intelligently chosen, leading to AI location solutions that are not
time-consuming to run and less likely to be plagued by over-fitting.
Experimental verification of our approach is provided. Our new algorithms are
directly applicable to location estimates in many scenarios including the
Internet of Things, and vehicular networks where vehicular GPS coordinates are
unreliable or need verifying. Our work represents the first successful AI
solution for a communication problem whose neural-network design is based on
fundamental information-theoretic constructs. We anticipate our approach will
be useful for a wide range of communication problems beyond location
estimation.
