CSI-based fingerprinting for localization using LTE signals

Nowadays, localization is becoming more and more an essential feature. The range of Location-Based Services (LBS) is rapidly expanding with the inclusion of several applications, which use real-time positioning data to provide information, such as health care, assisted living, fitness monitoring, building automation, security, retail, games and entertainment. The Global Navigation Satellite System (GNSS) represents the most common positioning technology, but it is well known that its accuracy and availability drop in many important application scenarios, such as indoor and urban canyons environments. However, users expect the same level of performance whether they are indoor at home or at work, outdoor in a rural or urban environment, or even traveling. To enhance positioning accuracy for all types of environment, on one hand there is the effort of the 3rd Generation Partnership Project (3GPP) to support more cellularbased localization techniques, such as Enhanced Cell IDentity (ECID), Assisted Global Navigation Satellite System (A-GNSS), Observed Time Difference Of Arrival (OTDOA) and Uplink-Time Difference Of Arrival (U-TDOA), defined in Releases 9 and 11 of the Long Term Evolution (LTE) standard. On the other hand, extensive work has been done in studying alternative localization solutions mainly based on other signals of opportunity, e.g. Frequency Modulation (FM) radio, Global System for Mobile communications (GSM), Universal Mobile Telecommunications System (UMTS), WiFi or dedicated sensors. In both cases, signal fingerprinting, also known as Radio-Frequency (RF) pattern matching, can play an important role, in fact it has also been included in LTE Release 9. Signal fingerprint-based localization techniques find the location of a device by comparing the signal pattern received from multiple transmitters, e.g. WiFi APs or cellular BSs, to a predefined database of signal patterns. 1 2 Most of the fingerprinting localization systems available in literature are based on the use of signal strength measures, which demonstrate high variability over time for a fixed location and do not exploit all the available information abput the wireless channel. For this reason, this work aims to provide better insights on the use of LTE signal for RF fingerprinting and in particular on the use of measurements that are not only related to the signal strength, but also to a finer-grained knowledge, at subcarrier level, of the channel gain, such as the one provided by the Channel State Information (CSI). As a matter of fact, the term CSI usually refers to WiFi and indicates the vectors of channel gains per subcarrier that can be extracted by commodity hardware. In this work, more generally, we call CSI a vector of channel gains per subcarrier that represents an estimate of the Channel Frequency Response (CFR) of the propagation channel. We suggest an LTE signal fingerprinting localization method that uses CSI vectors as fingerprint. Moreover, we also introduce a novel approach which is different from other CSI-related approaches that can be found in literature, since we propose to use as fingerprints not only the vectors of CSI, but also some "descriptors" of the "shape" of the CSI calculated on these vectors. This would greatly reduce the requirements in terms of memory for the database and also the computational complexity of the matching phase. The present work is organized as follows: Chapter 1 provides the theoretical background about RF signal fingerprinting, analyzes the state-of-art and introduces our main research contributions, Chapter 2 presents the fingerprinting approaches based on LTE CSI and, in particular, the novel descriptors method, while in Chapters 3 and 4 the experimental results respectively relative to device-based and device-free localization are shown and discussed.