# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
###### Abstract  
Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments. f 20  
which OTDOA relies, is limited by signal bandwidth. Second, NB-IoT devices are likely to operate in poor coverage scenarios (e.g., deep indoors), and this may negatively affect OTDOA positioning accuracy. Third, the adoption of OTDOA is hindered by the fact that, in most cases, operators have only recently completed or are still performing the deployment of Rel-13 NB-IoT networks [3]. The result is a growing interest in adopting different and novel strategies for NB-IoT positioning. In particular, radio frequency (RF) fingerprinting is an interesting candidate, given its reasonable requirements in terms of device capabilities and network deployment. Indeed, a favorable complexity-accuracy trade-off has made fingerprinting the most popular approach for indoor positioning based on WiFi [4]. Fingerprinting in LTE networks was originally proposed by 3 GPP in Technical Specification (TS) 36.809 [5], where RF and timing advance information were combined to define a fingerprint; the approach was however only tested via simulation. As is well known, fingerprinting operates in two phases. During the so-called offline phase, fingerprints, consisting in a set of RF data related to the detected access points of the technology in use (e.g., WiFi Access Points (APs) for WiFi and evolved Node Bs (eNBs) for LTE and/or NB-IoT) are collected at selected positions, referred to as reference points (RPs), and stored in a database. Note that in the case of LTE/NB-IoT, an eNB is associated with multiple physical cell identifiers (PCIs)/narrowband PCIs (NPCIs), each corresponding to a different signal source. Within the subsequent online phase, the location of a target device is estimated as a function of the position of the RPs associated to fingerprints that best match the one provided by the device according to a desired similarity metric, often defined as the inverse of a distance. Among several possible approaches to perform the estimation, \(k\) nearest neighbors (\(k\)NN) and its weighted version (\(Wk\)NN) are largely adopted owing to their reasonable complexity-accuracy trade-off [4]. The adoption of fingerprinting in large outdoor areas and using cellular networks poses, however, new, specific challenges both in the offline and online phases.