# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 1 Introduction and Motivation  
With the rapid development of communication technology, more and more electronic equipment is being connected to the internet, which has led to a rise in innovative applications in the smart everything everywhere, such as smart homes, smart cities, and so on. The widespread application of Global Navigation Satellite System (GNSS) technology (such as GPS, Galileo, GLONASS) has solved most of the outdoor positioning problems. However, the localization accuracy sharply declines once the receiver enters a non-line of sight (NLOS) environment. Therefore, indoor positioning systems have been extensively investigated, and intense efforts have been devoted to enhancing the localization performance [1; 2]. Among the current indoor positioning schemes, for instance, Radio Frequency Identification (RFID), Ultra-WideBand (UWB), Dead reckoning, image-based technology, and ultrasonic which strongly depend on extra devices, the Radio Frequency (RF) fingerprint positioning system has become a very promising and competitive technical solution for its high precision positioning by low-cost [3]. The strategy relies on the idea that every indoor location can be identified by a unique signal feature known as a fingerprint. Typical RF fingerprint (from now on, fingerprint) consists of radio measurements from multiple Access Points (AP), i.e., Received Signal Strengths (RSS) or path-loss measurements to provide a fingerprint of radio conditions at a specific location. The location of a fingerprint can be estimated using the known location of similar fingerprints previously recorded. One of the critical challenges to support such fingerprinting localization is to create and maintainaccurate fingerprint databases [4]. A site survey is often used to collect fingerprints from a targeted area. However, site surveying is a time-consuming and labor-intensive process. It requires several measurements of each fingerprint to obtain the statistical value of signal strength. Often professional work is needed for efficient site surveying. Moreover, a well-built fingerprint database become outdated as soon as the environment changes. For instance, the redistribution of APs after maintenance might degrade the accuracy of the fingerprint-based positioning system. To solve the problems mentioned above, some new kind of radio map construction techniques have been proposed, including data collection with the help of volunteers, simultaneous localization and mapping, propagation model prediction, RSS prediction based on exist fingerprints, fingerprint construction using passive crowdsourcing data [5], or by means of unsupervised techniques. The overall accuracy of Wi-Fi based fingerprint positioning is from few meters to around hundred meters [6; 7]. However, comparing the positioning accuracy of the different methods by using the published results of different studies might not be fair, since researchers are using different evaluation environments, methodologies and test-beds. Although some traditional works report that the accuracy of Wi-Fi based fingerprint positioning is around 2-3 m [8; 9], it can reach a higher averaged error when the fingerprint density (including the distance between reference points and/or number of fingerprints per reference point) in the radio map is not dense enough [10] or when external resources (e.g., floor plan, magnetometer, barometer, inertial sensors) are not considered [11; 12; 13]. Thus, the accuracy highly depends on the scenario (building materials, obstacles, presence of people, among others) and on the quality of the generated radio map (deployed anchors/APs, number of independent fingerprints per reference point, distance between reference points, device diversity, among many other features). Some previous works have identified that the noise present in the radio map and the operational fingerprint is one of the primary sources of the positioning errors [14].