Statistics

Orientation (vector space)

Real-time computing

Telecommunications

Computer science

Geology

Computer network

Fingerprint (computing)

Pattern recognition (psychology)

Mathematics

Channel state information

Artificial intelligence

Geometry

Remote sensing

Physical layer

Deep learning

Signal strength

Channel (broadcasting)

Wireless

Mean squared error

IEEE Access

Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements. In this paper, we propose to use a mid-grained intermediate-level channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the IEEE 802.11ad/ay standards - to construct the fingerprinting database. These intermediate channel measurements are further utilized by a deep learning approach for multiple purposes: 1) location-only classification; 2) simultaneous location-and-orientation classification; and 3) direct coordinate estimation. Furthermore, the effectiveness of the framework is thoroughly validated by an in-house experimental platform consisting of 3 access points using commercial-off-the-shelf millimeter-wave WiFi routers. The results show a 100% accuracy if the location is only interested, about 99% for simultaneous location-and-orientations classification, and an averaged root mean-square error (RMSE) of 11.1 cm and an average median error of 9.5 cm for direct coordinate estimate, greater than 2-fold improvements over the RMSE of 28.7 cm and median error of 23.6 cm for RSSI-like single SNR-based localization.

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach