119 research outputs found
D2D-based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey
Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research
Fingerprint positioning of users devices in long term evolution cellular network using K nearest neighbour algorithm
The rapid exponential growth in wireless technologies and the need for public safety has led to increasing demand for location-based services. Terrestrial cellular networks can offer acceptable position estimation for users that can meet the statutory requirements set by the Federal Communications Commission in case of network-based positioning, for safety regulations. In this study, the proposed radio frequency pattern matching (RFPM) method is implemented and tested to determine a user’s location effectively. The RFPM method has been tested and validated in two different environment. The evaluations show remarkable results especially in the Micro cell scenario, at 67% of positioning error 15m and at 90% 31.78m for Micro cell scenario, with results of 75.66m at 67% and 141.4m at 90% for Macro cell scenario
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
Enhanced Integrated Indoor Positioning Algorithm Utilising Wi-Fi Fingerprint Technique
This paper describes an integrated positioning algorithm utilizing Wi-Fi fingerprint technique for indoor positioning. The main contribution of this work is the improvement of positioning accuracy for indoor localization even in extreme RSSI fluctuation which leads to variation of positioning error. Several layers of Wi-Fi positioning is proposed, which are based on deterministic techniques, iterative Bayesian estimation, and also Kalman filter to enhance accuracy due to noise presence. Here, accumulated accuracy is introduced where the distribution of location error is determined by estimation at each test point on path movement. The results show that the integrated algorithm enhances the estimation accuracy in several scenarios which are different Wi-Fi chipsets and movement directions. The error distribution shows an achievement of up to 65% for error less than 5m compared to the basic deterministic technique of only 45%
- …