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Non-parametric Smart Sensing Analytics based on Large Spectrum Data and Estimation of Channel Activity Statistics


The main objective of this project is to develop feasible non-parametric smart sensing mechanisms with an improved performance obtained by exploiting statistical knowledge of the spectrum activity patterns, and validate their suitability by means of a proof-of-concept wireless prototype / test bed.

Description

Future wireless networks will demand huge amounts of radio frequency spectrum resources. It is unlikely that such demand will be met without employing smart dynamic spectrum sharing approaches based on cognitive radio (CR) techniques. In this context, one important requirement of future wireless networks will be the ability to detect the presence of other wireless systems within a particular region of the spectrum occupancy data. Spectrum sensing is a popular approach to address this problem and constitutes a fundamental building block of CR systems. Existing sensing schemes are parametric and imperfect in nature, and unrealistic to implement on large scale networks due to various practical performance limitations. The main objective of this project is to develop feasible non-parametric smart sensing mechanisms with an improved performance obtained by exploiting statistical knowledge of the spectrum activity patterns, and validate their suitability by means of a proof-of-concept wireless prototype / test bed.

This project is funded by DST-UK-India Education and Research Initiative (UKIERI), British Council.

Faculty

Other Members

  • Dr. Miguel López-Benítez (University of Liverpool -UK)
  • Brijesh Soni (JRF)