# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 2 Background and Related Work  
In LTE, the OTDOA algorithm is implemented and run at the network side and is based on time of arrival (TOA) estimates performed by the user equipment (UE) on the periodic downlink position reference signal (PRS) [11; 12]. Given a set \(\mathcal{S}\) of \(S\) eNBs within its range, the UE selects a reference eNB in the set and reports to the network \(S-1\) TDOA measurements, obtained as the difference between the PRS TOA estimates of each of the remaining \(S-1\) eNBs in \(\mathcal{S}\) and of the reference eNB, and is referred to as the reference signal time difference (RSTD). The OTDOA algorithm has encountered a widespread adoption in LTE networks worldwide, with reported positioning error in the order of 35 m at the 90% percentile [13; 14]. Fingerprinting in LTE was proposed by 3 GPP [5] and experimentally validated in [15]. Several extensions were then proposed. In particular, fingerprinting was enhanced in [16] owing to the introduction of RSTD information in the definition of the fingerprint that improved the accuracy over OTDOA. Ever since Rel-14, NB-IoT uses a dedicated signal, called narrowband PRS (NPRS) to compute OTDOA, similarly to LTE. NPRS can be transmitted continuously (while PRS in LTE is limited to up to six consecutive subframes [17]) in order to compensate for the loss of accuracy due to a narrower signal bandwidth than LTE. Despite the introduction of NPRS, TOA accuracy in NB-IoT is expected to be severely degraded compared to LTE [17]. Recent investigations have proposed and analyzed, via simulation, possible enhancements toward improving the TOA estimation accuracy for NB-IoT at the price, however, of a lack of compliance with the standard and increased complexity (e.g., see [18; 19; 20]). The first study proposing fingerprinting using NB-IoT signals was presented in [6], where devices transmitting signals compliant to the NB-IoT standard in terms of base-band data content, bandwidth, and carrier frequency were deployed in an indoor location and used to build a fingerprinting system covering the same location. Albeit interesting, this work did not cover the most realistic use case of fingerprinting with NB-IoT; that is, covering a large outdoor area served by commercial NB-IoT operators. The earliest contribution addressing such a use case was presented in [7], where RSSI measurements defined fingerprints, and a WkNN algorithm--to be used in the online phase--was compared against two other methods: (a) a ranging-based algorithm that uses RSSI to estimate distances between UE and eNB, and (b) a proximity algorithm that matches the position of the UE with an estimate of the position of the eNB detected with highest RSSI. Experimental data collected using commercial NB-IoT devices were used to evaluate the performance of the three above algorithms and showed that RSSI-based fingerprinting outperforms both RSSI-based ranging and RSSI-based proximity algorithms. The best results were obtained with the Pearson \(\chi^{2}\) coefficient as the similarity metric, with an average positioning error of about 184 m. Due to limitations in the hardware used to collect data, the fingerprints defined in [7] only included the RSSI of the cell to which the NB-IoT device was connected and thus only included information on a single cell. However, because of cell (re)selection purposes, the NB-IoT standard foresees that a device may collect data on multiple cells at the same time, similarly to what is commonly done by WiFi and LTE devices [1]. In areas characterized by high spatial network density, this may result in a large number of data elements being stored in each fingerprint. Since the amount of information in a fingerprint is a key element in determining positioning accuracy [21], the introduction of multiple cell (in the following: multicell) information in the definition of fingerprints is expected to strongly improve performance with respect to [7].