2,032 research outputs found

    Waveform flexibility in database-oriented cognitive wireless systems

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    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper we discuss the idea of waveform flexibility in future wireless networks utilizing cognitive radio functionality. Mainly, we consider the possibility to adjust the shape of the waveform based on the information about the surrounding environment stored in a dedicated context-information database. In our approach, the cognitive terminal has an option to select one of four available waveforms to adapt itself in the best way to the constraints delivered by the database. In this paper we present the key concept of waveform flexibility, the proposed algorithm for waveform selection and the achieved simulation results.Peer ReviewedPostprint (author's final draft

    Efficient Radiometric Signature Methods for Cognitive Radio Devices

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    This thesis presents the first comprehensive study and new methods for radiometric fingerprinting of the Cognitive Radio (CR) devices. The scope of the currently available radio identification techniques is limited to a single radio adjustment. Yet, the variable nature of the CR with multiple levels of parameters and adjustments renders the radiometric fingerprinting much more complex. We introduce a new method for radiometric fingerprinting that detects the unique variations in the hardware of the reconfigurable radio by passively monitoring the radio packets. Several individual identifiers are used for extracting the unique physical characteristics of the radio, including the frequency offset, modulated phase offset, in-phase/quadrature-phase offset from the origin, and magnitude. Our method provides stable and robust identification by developing individual identifiers (classifiers) that may each be weak (i.e., incurring a high prediction error) but their committee can provide a strong classification technique. Weighted voting method is used for combining the classifiers. Our hardware implementation and experimental evaluations over multiple radios demonstrate that our weighted voting approach can identify the radios with an average of 97.7% detection probability and an average of 2.3% probability of false alarm after testing only 5 frames. The probability of detection and probability of false alarms both rapidly improve by increasing the number of test frames

    Challenges of Radio Frequency Fingerprinting: From Data Collection to Deployment

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    Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. However, integrating DL techniques with RFF and operating the system in real-world scenarios presents numerous challenges. This article identifies and analyzes these challenges while considering the three reference phases of any DL-based RFF system: (i) data collection and preprocessing, (ii) training, and finally, (iii) deployment. Our investigation points out the current open problems that prevent real deployment of RFF while discussing promising future directions, thus paving the way for further research in the area.Comment: 7 pages, 1 table, and 4 figure
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