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Homonuclear correlation spectroscopy, or COSY, is a 2-dimensional NMR technique that provides information about coupled protons. Typically, the geminal and vicinal coupling are observed. For example, consider the COSY spectrum of ethyl acetate, where its 1D proton NMR spectrum is plotted along the vertical and horizontal axes with their corresponding chemical shift scale. Three spots on the diagonal corresponding to the three peaks in the 1D proton spectrum are called diagonal peaks. The COSY...
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Hydrocarbons such as alkanes, alkenes, and alkynes show characteristic C–H stretching absorption bands. These IR stretching frequencies depend on the hybridization of the involved carbon atom and can be explained in terms of the s character of each hybridized atomic orbital.
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The Electromagnetic Spectrum

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Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks.

Lakshminarayanan Vaduganathan1, Shubhangi Neware2, Przemysław Falkowski-Gilski3

  • 1Department of Electrical and Electronics Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India.

Entropy (Basel, Switzerland)
|September 28, 2023
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Summary

Cognitive radio networks (CRNs) improve spectrum access. This study introduces a component-specific cooperative spectrum sensing model (CSCSSM) for more efficient and robust spectrum sensing in CRNs.

Keywords:
cognitive radio networkscomponent-specific adaptive estimationpower spectrumprimary usersspectrum sensing

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Cognitive Radio Networks

Background:

  • Rapid advancements in wireless communication necessitate efficient spectrum utilization.
  • Cognitive radio networks (CRNs) enable dynamic access to underutilized spectrum bands.
  • Spectrum sensing (SS) is crucial for CRNs to identify available frequencies.

Purpose of the Study:

  • To develop an improved cooperative spectrum sensing (CSS) model for CRNs.
  • To enhance the accuracy of spectrum sensing by considering signal components.
  • To minimize information loss during spectrum sensing.

Main Methods:

  • Introduced a component-specific cooperative spectrum sensing model (CSCSSM).
  • Incorporated Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation.
  • Considered amplitude and phase components of the input signal for estimation.

Main Results:

  • The proposed CSCSSM minimizes information loss compared to traditional methods.
  • Experimental analysis demonstrates the robustness of the new model.
  • The CSAE approach proves efficient in parameter estimation for spectrum sensing.

Conclusions:

  • The CSCSSM offers a more effective approach to spectrum sensing in CRNs.
  • The method enhances the performance and reliability of cognitive radio systems.
  • Accurate power spectrum approximation is vital for efficient CRN operation.