  Localization of wireless transmitters based on channel state information
(CSI) fingerprinting finds widespread use in indoor as well as outdoor
scenarios. Fingerprinting localization first builds a database containing CSI
with measured location information. One then searches for the most similar CSI
in this database to approximate the position of wireless transmitters. In this
paper, we investigate the efficacy of locality-sensitive hashing (LSH) to
reduce the complexity of the nearest neighbor-search (NNS) required by
conventional fingerprinting localization systems. More specifically, we propose
a low-complexity and memory efficient LSH function based on the sum-to-one
(STOne) transform and use approximate hash matches. We evaluate the accuracy
and complexity (in terms of the number of searches and storage requirements) of
our approach for line-of-sight (LoS) and non-LoS channels, and we show that LSH
enables low-complexity fingerprinting localization with comparable accuracy to
methods relying on exact NNS or deep neural networks.
