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6 - Subsampling multi-standard receiver design for cognitive radio systems

from Part II - Adaptable receivers for white space technologies

Published online by Cambridge University Press:  05 October 2014

By
Abul Hasan ,
Mohamed Helaoui  and
Fadhel M. Ghannouchi
Edited by
Nuno Borges Carvalho ,
Alessandro Cidronali  and
Roberto Gómez-García
Abul Hasan
Affiliation:
University of Calgary
Mohamed Helaoui
Affiliation:
University of Calgary
Fadhel M. Ghannouchi
Affiliation:
University of Calgary
Nuno Borges Carvalho
Affiliation:
Universidade de Aveiro, Portugal
Alessandro Cidronali
Affiliation:
Università degli Studi di Firenze, Italy
Roberto Gómez-García
Affiliation:
Universidad de Alcalá, Madrid
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Summary

Introduction

Cognitive radio (CR), as coined and defined by its first proponent, is the integration of model-based reasoning with software radio techniques [1]. An important aspect of cognitive radio is the efficient use of resources, especially the frequency spectrum, in a typical communication environment. Spectrum management requires spectrum sensing; the subsampling technique has been demonstrated to be an efficient approach for spectrum sensing for CR applications [2]. Cognitive radio technology, in the context of white space, has been discussed in Chapter 1 of this book. Situation awareness and learning capability are some of the features in a CR through which it becomes aware of the location, radio frequency (RF) environment, and updates its knowledge. Environmental information in a CR is typically provided by an in-built or network-enabled radio environment map (REM) through some learning process. A typical CR node consists of the RF front-end and configurable hardware and software platform. The current software-defined radio (SDR) platforms will facilitate the evolution of CR by adding cognitive and intelligent features to it with the help of cognitive engines (CE). Cognitive engines are essentially the software packages that facilitate the cognitive feature to an agile radio platform.

A software-defined radio (SDR) is a radio that can accommodate a significant range of RF bands and air interface modes through software [1]. An ideal SDR receiver will sample and digitize the RF signals as close as possible to the receiver antenna.

Type
Chapter
Information
White Space Communication Technologies , pp. 167 - 196
Publisher: Cambridge University Press
Print publication year: 2014

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References

[1] J., Mitola, III, “Cognitive Radio,” Licentiate Thesis, KTH, Royal Institute of Technology, Stockholm, Sweden, September 1999.
[2] A., Kwan, S. A., Bassam, and F. M., Ghannouchi, “Sub-Sampling Technique for Spectrum Sensing in Cognitive Radio Systems,”2012 IEEE Radio and Wireless Symposium (RWS), pp. 347–350, January 2012.Google Scholar
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[7] J. R. G., Oya, A., Kwan, F., Munoz, F. M., Ghannouchi, M., Helaoui, F. M., Lasso, E., Lopez-Morillo, and A., Torralba, “Subsampling Receivers with Applications to Software Defined Radio Systems, Data Acquisition Applications,”Data Acquisition Applications, InTech, 2012.Google Scholar
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[11] R., Barrak, A., Ghazel, and F. M., Ghannouchi, “Optimized Multistandard RF Subsampling Receiver Architecture,”IEEE Transactions on Wireless Communications, vol. 8, no. 6, pp. 2901–2909, June 2009.Google Scholar
[12] J. R. G., Oya, F., Munoz, A., Torralba, A., Jurado, F. J., Marquez, and E., Lopez-Morillo, “Data Acquisition System Based on Subsampling Using Multiple Clocking Techniques,”IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 8, pp. 2333–2335, August 2012.Google Scholar
[13] J. R. G., Oya, F., Munoz, A., Torralba, A., Jurado, A. J., Garrido, and J., Banos, “Data Acquisition System Based on Subsampling for Testing Wideband Multistandard Receivers,”IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 9, pp. 3234–3237, September 2011.Google Scholar
[14] J. R. G., Oya, A., Kwan, S. A., Bassam, F., Munoz, and F. M., Ghannouchi, “Optimization of Subsampling Dual Band Receivers Design in Nonlinear Systems,”2012 IEEE International Microwave Symposium Digest, pp. 1–3, 17-22 June 2012.Google Scholar

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