Survey of surveys on spectrum sensing for Cognitive Radio

Several reviews on spectrum sensing techniques for cognitive radio have been published. In this post I comment three recent surveys that focus on different aspects of the problem of primary user detection in cognitive radio environments, finding that some important (at least to me) questions are surprisingly left out.

[Y09] offers a high level overview of spectrum sensing for cognitive radio describing the main challenges (such as the hidden node problem, detection of spread spectrum users...) and classifying the different sensing methods as shown in the following figure:


We can see that while they maintain the classical division into Energy detection, Cyclostationarity detection and Matched filter detectors; they also introduce the concepts of Waveform-based sensing and Radio identification.

Waveform-based sensing refers to the detection of known transmitted patterns such as pilots or preambles, and of course increases the accuracy of the detector with a relatively low complexity. On the other hand radio identification refers to using the available a priori knowledge about the transmitter technology we are interested in (such as transmission range, frequency hops, spectral shape...).

The topics covered in [Y09] also include cooperative detection and how to use historic data, such as temporally correlated traffic, to improve detection performance. Additionally this survey includes an interesting part dedicated to how Spectrum Sensing is performed in current wireless standards, including IEEE 802.11k, Bluetooth or IEEE 802.22.


[A09] is a short review focusing on spectral estimation. Different approaches to power spectral density (psd) reconstruction are discussed, including pilot detection, multi taper spectrum estimation or filter banks. Once the psd has been estimated it can be used to detect primary users in cognitive radio systems.


Finally, [Z10] focuses on technical aspects of detection theory applied to cognitive radio environments. This includes using space and time correlation, cyclostationarity detection or cooperative sensing. Other more involved topics are threshold derivation, noise power uncertainty, or what they call robust spectrum sensing. Robust spectrum sensing theory is useful when the a priori knowledge of the noise or signal distribution is limited or imprecise.

The section Future Developments in [Z10] poses some problems related to primary user detection that did not get so much attention from the research community. Here is briefly presented one of the points I missed in all these reviews, namely wideband spectral sensing. Most of the articles cited in these reviews focus on the problem of detecting a single primary user present in the band of interest, however, in general it can be expected that the spectral monitor simultaneously processes multiple channels, and thus more sophisticated detection techniques have to be used (such as, e.g. compressive sensing).


Remember that another way to keep track of the most recent research on spectrum sensing is to take a look to key conferences on cognitive radio, such as the marathonian spectrum sensing sessions at the ICASSP 2010.

[Y09]	T. Yucek and H. Arslan A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, Volume: 11 Issue:1. First Quarter 2009.
[A09]	D.D. Ariananda, M.K. Lakshmanan and H. Nikoo A survey on spectrum sensing techniques for Cognitive Radio. Second International Workshop on Cognitive Radio and Advanced Spectrum Management, May 2009. CogART 2009.
[Z10]	Y. Zeng, Y. Liang, A. T. Hoang, and R. Zhang A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions. EURASIP Journal on Advances in Signal Processing Volume 2010.