Frequency domain interpretation of power ratio metric for cognitive radio systems

Hussain, S. , Palicot, J., Louët, Y. and Zabré, S. (2008) Frequency domain interpretation of power ratio metric for cognitive radio systems. IET Communications, 2(6), pp. 783-793. (doi:10.1049/iet-com:20070478)

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Abstract

SoftWare radio (SWR) is an enabling technology for cognitive radio (CR) systems which promises to (de) modulate any signal, at any frequency. SWR signal therefore is composed of different standard's signals, and each standard's signal is either multicarrier or multiplex of single carriers. This combination leads to high temporal fluctuations and thus SWR signal inherits high peak to average power ratio (PAPR) or simply high power ratio (PR). Nonlinear analogue components (amplifiers, converters etc.) cause distortions (in and out of band distortion) for high PR signals which result in system performance degradation. Usually PR problem is addressed in time domain, and here frequency domain interpretation of PR which is more appropriate in SWR context is presented. Gaussian equivalence between SWR signal and orthogonal frequency division multiplexing (OFDM) signal is proved first to accentuate high PR issue in SWR as OFDM suffers the same problem. Then frequency domain interpretation of PR metric is discussed which results in a PR upper bound. This PR upper bound depends only upon spectral values of the signal thus associates spectrum with PR. As a result this bound assists in spectrum access for CR systems by providing PR metric information related to any available bandwidth. Thus bandwidth allocation in a spectrum access scenario under PR constraint is simplified.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Hussain, Dr Sajjad
Authors: Hussain, S., Palicot, J., Louët, Y., and Zabré, S.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:IET Communications
Publisher:The Institution of Engineering and Technology
ISSN:1751-8628
ISSN (Online):1751-8636

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