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High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis
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A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed.

Robert Qiu1, Nan Guo, Husheng Li

  • 1Department of Electrical and Computer Engineering, Center for Manufacturing Research, Tennessee Technological University, Cookeville, TN 38505, USA; E-Mails: nguo@tntech.edu (N.G.); ysong21@tntech.edu (Y.S.); Vasu.Chakravarthy@wpafb.af.mil (V.C.); zhu21@tntech.edu (Z.H.); pzhang21@tntech.edu (P.Z.); zchen42@tntech.edu (Z.C.).

Sensors (Basel, Switzerland)
|March 29, 2012
PubMed
Summary

Future cognitive sensors require dynamic spectrum access. This paper explores the convergence of wideband cognitive radio and radar, covering algorithms, hardware, multi-GHz front ends, compressed sensing, machine learning, and detection for advanced multi-function systems.

Keywords:
cognitive radarcognitive radiotestbed

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Dynamic spectrum access is crucial for advanced cognitive sensors.
  • The convergence of cognitive radio and radar presents new opportunities and challenges.
  • Moore's Law enables increased system functionality in digital components.

Purpose of the Study:

  • To provide a tutorial on wideband cognitive radio and radar convergence.
  • To address the challenges of multi-function, multi-GHz front ends for next-generation cognitive sensors.
  • To explore the integration of cognitive radio, radar, and anti-jamming capabilities.

Main Methods:

  • Survey of algorithms and hardware platforms for cognitive radio and radar.
  • Analysis of compressed sensing techniques for multi-GHz waveforms and analog-to-digital conversion.
  • Exploration of machine learning applications in cognitive radio and radar systems.
  • Discussion of quickest detection methods and overlay/underlay cognitive radio waveforms.

Main Results:

  • Identifies the multi-GHz front end as a key challenge for next-generation cognitive sensors.
  • Highlights the convergence of cognitive radio, radar, and anti-jamming as a unifying theme.
  • Provides a comprehensive system-level treatment of functions and challenges in multi-function wideband systems.

Conclusions:

  • The convergence of cognitive radio and radar, enabled by advancements like compressed sensing and machine learning, is essential for future cognitive sensors.
  • Addressing the multi-GHz front-end challenges is critical for realizing these advanced multi-function systems.
  • This work integrates inter-disciplinary knowledge to offer a holistic view of this evolving field.