Compressed sensing techniques for receiver based post-compensation of transmitter's nonlinear distortions in OFDM systems
Type
ArticleAuthors
Owodunni, Damilola S.Ali, Anum Z.
Quadeer, Ahmed Abdul
Al-Safadi, Ebrahim B.
Hammi, Oualid
Al-Naffouri, Tareq Y.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2014-04Permanent link to this record
http://hdl.handle.net/10754/563463
Metadata
Show full item recordAbstract
In this paper, compressed sensing techniques are proposed to linearize commercial power amplifiers driven by orthogonal frequency division multiplexing signals. The nonlinear distortion is considered as a sparse phenomenon in the time-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional compressed sensing approach, while the second incorporates a priori information about the distortions to enhance the estimation. Finally, the third technique involves an iterative data-aided algorithm that does not require any pilot carriers and hence allows the system to work at maximum bandwidth efficiency. The performances of all the proposed techniques are evaluated on a commercial power amplifier and compared. The error vector magnitude and symbol error rate results show the ability of compressed sensing to compensate for the amplifier's nonlinear distortions. © 2013 Elsevier B.V.Citation
Owodunni, D. S., Ali, A., Quadeer, A. A., Al-Safadi, E. B., Hammi, O., & Al-Naffouri, T. Y. (2014). Compressed sensing techniques for receiver based post-compensation of transmitter’s nonlinear distortions in OFDM systems. Signal Processing, 97, 282–293. doi:10.1016/j.sigpro.2013.10.029Sponsors
This work was supported by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals through Project no. 11-ELE1651-04 as part of the National Science, Technology and Innovation Plan.Publisher
Elsevier BVJournal
Signal Processingae974a485f413a2113503eed53cd6c53
10.1016/j.sigpro.2013.10.029