Wi-Fi fingerprinting-based indoor localization has received increased attention due to its proven accuracy and global availability. The common received-signal-strength-based (RSS) fingerprinting presents performance degradation due to well-known signal fluctuations, but more recently, the more stable channel state information (CSI) has gained popularity. In this paper, we present SDR-Fi, the first reported Wi-Fi software-defined radio (SDR) receiver for indoor positioning using CSI measurements as features for deep learning (DL) classification. The CSI measurements are obtained from a fast-prototyping LabVIEWbased 802.11n SDR receiver platform. SDR-Fi measures CSI data passively from pilot beacon frames from a single access point (AP) at almost 10 Hz rate. A feed-forward neural network and a 1D convolutional neural network are examined to estimate location accuracy in representative testing scenarios for an indoor cluttered laboratory area, and an adjacent, covered outdoor area. The proposed DL classification methods leverage CSI-based fingerprinting for low AP scenarios, as opposed to traditional RSS-based systems, which require many APs for reliable positioning. Demonstration results are threefold: (a) A fast-prototyping SDR platform that passively extracts CSI measurements from Wi-Fi beacon frames, providing a genuine possibility for vendor network cards to provide such measurements, (b) two state-of-the-art DL classification methods outperforming traditional RSS-based methods for low AP scenarios, (c) a testing methodology for performance evaluation of the proposed indoor positioning system.INDEX TERMS Channel state information, deep learning, fingerprinting, indoor positioning, neural networks, software-defined radio.
He is currently a Ph.D. candidate in the Department of Electrical and Computer Engineering, at the University of Texas at San Antonio (UTSA). His research interests include implementation of platforms for software-defined radio algorithms for fast prototyping, WLAN indoor localization systems, and interference mitigation techniques for Global Navigation Satellite System (GNSS). He is a graduate student member of IEEE and ION.
The U.S. global position system (GPS) is one of the existing global navigation satellite systems (GNSS) that provides position and time information for users in civil, commercial and military backgrounds. Because of its reliance on many applications nowadays, it's crucial for GNSS receivers to have robustness to intentional or unintentional interference. Because most commercial GPS receivers are not flexible, software-defined radio emerged as a promising solution for fast prototyping and research on interference mitigation algorithms. This paper provides a proposed minimum mean-squared error (MMSE) interference mitigation technique which is enhanced for computational feasibility and implemented on a real-time capable GPS L1 SDR receiver. The GPS SDR receiver SW has been optimized for real-time operation on National Instruments' LabVIEW (LV) platform in conjunction with C/C++ dynamic link libraries (DLL) for improved efficiency. Performance results of said algorithm with real signals and injected interference are discussed. The proposed SDR receiver gains in terms of BER curves for several interferers are demonstrated.
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