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Recent Advances on Wideband Spectrum Sensing for Cognitive Radio

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Cognitive Communication and Cooperative HetNet Coexistence

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Spectrum sensing plays a fundamental role in cognitive radio (CR) networks allowing to discover spectrum opportunities and enabling primary user (PU) protection. However, it represents also one of its most challenging aspects due to the requirement of performing radio environment analysis in a short observation time and the fact that its performance can be strongly affected by harsh channel conditions and lack of knowledge about the PU characteristics. In literature, many techniques have been proposed, starting from the most popular algorithms, such as energy detection, to the most advanced, such as, e.g., eigenvalue based detection and cooperative approaches. Most of these techniques have been conceived to assess the occupancy of PUs within a single frequency band. A better knowledge of the surrounding radio environment can be reached exploiting wideband spectrum sensing, that consists in a joint observation of multiple bands and joint detection on the occupancy of each sub-band. Recently, different wideband approaches have been proposed, mainly derived from advanced spectral analysis techniques such as multitaper methods and compressive sensing. In this chapter, we propose a novel methodology for wideband spectrum sensing based on the computation of a frequency domain representation of the received samples and the use of information theoretic criteria (ITC) to identify which frequency components contain PU signals. This technique does not require the setting of a decision threshold, a problem for many spectrum sensing algorithms due to dependence on unknown parameters or difficulties in the statistical description of the decision metrics. We provide a general formulation of the problem, valid for any kind of spectral representation and then focus on the case in which discrete Fourier transform (DFT) is used. This choice is motivated by the simplicity of implementation and the fact that DFT blocks are already available in many wireless systems, such as OFDM receivers. This wideband spectrum sensing approach can be adopted by a single CR node in a standalone manner or within a cooperative sensing scheme. Numerical results show that the algorithm derived for DFT can be also applied as an approximated approach when more accurate frequency representations, such as multitaper method (MTM) spectrum estimates, are adopted. Wideband ITC based sensing can be applied in scenarios in which approaches that require a high level of sparsity of the received signal (such as compressive sensing) can not be adopted.

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Notes

  1. 1.

    The SUs are unlicensed or light-licensed users; in the former case the expression “opportunistic unlicensed access” is often used [2].

  2. 2.

    The expression “white space” is mainly used with reference to digital television (DTV) bands. It is however accepted as a general term [4].

  3. 3.

    Note that cooperative sensing schemes could be included in the class of the diversity based algorithms, because the adoption of several sensing nodes is essentially a technique for exploiting spatial diversity. However, we separate the class of cooperative algorithms because they have some peculiar characteristics that are not common to other diversity based techniques, such as the selection of the fusion strategy to be adopted, presence of error prone reporting channels, unbalances in the average received power, etc..

  4. 4.

    See footnote 3.

  5. 5.

    Note that in some works the term “distributed” is used as a synonym of cooperative, and expressions such as “non-centralized” are adopted.

  6. 6.

    Generally, the unique assumption is that the received signal samples are taken from a stationary random process.

  7. 7.

    This is in accordance to the DFT based scenario studied in the following.

  8. 8.

    We will refer to the \(k\)-th model also as the \(k\)-th hypothesis.

  9. 9.

    Varying the number of occupied frequency bins we have a different set of parameters that describe the model [64].

  10. 10.

    Using the notation \(\fancyscript{P}{}\left( k\right) \) we emphasize that the penalty depends on \(k\) through the vector \(\widehat{\varvec{\varTheta }}^{\left( k\right) }\). Note that in general \(\fancyscript{P}{}\left( k\right) \) could also depend on other parameters, e.g. \(N_\mathrm{b }\), \(N\) and other functions of the observation.

  11. 11.

    Including the channel effects.

  12. 12.

    This is a proper assumption for many practical problems, such as the case of OFDM signals, that are widely adopted in recent communication systems.

  13. 13.

    Numerical simulations show that the difference between \(P_{\text {k}}\) and the probability of correctly identifying the set of occupied frequency bins is very small, which means that when the algorithms correctly estimate \({k}^{*}\) they generally correctly estimate also the occupied set. See [17] for some numerical examples.

  14. 14.

    Note that in some practical applications the adoption of algorithms that tend to overestimate \({k}^{*}\) may be used by means of including a protection margin to preserve low SNR PU transmissions.

  15. 15.

    For simplicity, here we do not use the exact distribution of the ordered vector. Thus the ENP-ED approach adopted can be considered as an approximated strategy valid for large samples use cases.

  16. 16.

    Note that this approximation is valid when the chi squared distribution has a high number of degrees of freedom.

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Authors and Affiliations

  1. Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” - DEI, Alma Mater Studiorum University of Bologna, Via Venezia 52, 47521, Cesena, FC, Italy

    Andrea Mariani, Andrea Giorgetti & Marco Chiani

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  1. Andrea Mariani
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  2. Andrea Giorgetti
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  3. Marco Chiani
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Correspondence to Andrea Mariani .

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  1. Rome, Italy

    Maria-Gabriella Di Benedetto

  2. Rennes, France

    Faouzi Bader

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Mariani, A., Giorgetti, A., Chiani, M. (2014). Recent Advances on Wideband Spectrum Sensing for Cognitive Radio. In: Di Benedetto, MG., Bader, F. (eds) Cognitive Communication and Cooperative HetNet Coexistence. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-01402-9_1

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