Compressed Power Spectral Density Estimation via Group-Based Total Variation Minimization Conference Paper

abstract

• 2016 IEEE. Cognitive radio requires power spectral density (PSD) estimation in order to detect the inactive bands for opportunistic secondary usage. In this paper, we present a new compressed power spectral estimation scheme via group-based total variation minimization. Leveraging the intra-group total variation penalty, piecewise-constant characteristics in the signal power spectrum are modeled to improve accuracy of estimation. The effectiveness of the proposed technique is illustrated using simulation of complex baseband OFDM signals. The results show that the proposed scheme has lower power spectral estimation error at the same compressed sampling rate and signal-to-noise ratio compared to previous state of the art compressive spectrum sensing methods.

name of conference

• 2016 IEEE International Workshop on Signal Processing Systems (SiPS)

authors

• Hoyos, Sebastian

published proceedings

• 2016 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS)

author list (cited authors)

• Zhou, J., Wang, Z., & Hoyos, S.

citation count

• 0

complete list of authors

• Zhou, Jun||Wang, Zhongfeng||Hoyos, Sebastian

publication date

• October 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation  Journal

keywords

• Compressive Sensing
• Group-based Total Variation
• Power Spectral Density Estimation

Digital Object Identifier (DOI)

• 10.1109/SiPS.2016.9

International Standard Book Number (ISBN) 13

• 9781509033614

start page

• 7

end page

• 10

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Fsips.2016.9