Results 21 to 30 of about 15,047 (284)
SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems [PDF]
In this paper, the software and hardware of a software-defined radio (SDR) platform are used to implement and verify the blind real-time sensing act of intelligent collaborative spectrum sensing based on a new theoretical formula for constructing ...
Ali Radhi, Hanan Akkar, Hikmat Abdullah
doaj +1 more source
RF-MEMS technology has evolved significantly over the years, during which various attempts have been made to tailor such devices for extreme performance by leveraging novel designs and fabrication processes, as well as integrating unique materials ...
Rayan Bajwa, Murat Kaya Yapici
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Radio frequency interference detection using machine learning
Radio frequency interference (RFI) has plagued radio astronomy which potentially might be as bad or worse by the time the Square Kilometre Array (SKA) comes up. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by man.
Mosiane, Olorato +2 more
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Scalable Machine-Learning-Based Classification of Radio Frequency Building Loss
En noggrann modellering av utomhus-till-inomhus (O2I) och inomhus-tillinomhus (I2I) signalförluster är avgörande för att förbättra prestandan hos trådlösa inomhusnät i täta stadsmiljöer. Traditionella mätningar på plats är kostsamma, tidskrävande och svåra att skala upp. Realtidsdatamängder lider ofta av brus och obalans, vilket försvårar tillförlitlig
Tan, Jiayi
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UHF RFID Channel Emulation Testbed for Wireless IoT Systems
We propose an ultra high frequency (UHF) radio frequency identification (RFID, 902–928 MHz in the US) channel emulation testbed that is capable of simultaneously emulating unique wireless channels.
Md. Abu Saleh Tajin +4 more
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Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However,
Yi Xia +3 more
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Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders
The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to the radio spectrum is becoming increasingly more important.
Siddhartha Subray +2 more
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Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning
9 pages, 5 figures; submitted to ACM SIGCOMM ...
Forough Yaghoubi +4 more
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While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to ...
Lauren J. Wong +5 more
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Specific Emitter Identification Based on Software-Defined Radio and Decision Fusion
Specific emitter identification (SEI) uses the unintentional modulation information carried by the emitter waveform, i.e., radio frequency fingerprints, to realize the matching identification of the received signal and its corresponding emitter.
Kaiwen Tan +4 more
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