### Quick survey on wideband spectrum sensing for CR

As I commented in the post about recent surveys on spectrum sensing I miss a review of the different approaches to

Similarly A. Taherpour, S. Gazor, and M. Nasiri-Kenari propose in "Wideband spectrum sensing in unknown white Gaussian noise” a distributed network of secondary users that collaborate in order to detect multiple primary users under the assumption that the level of each frequency–domain subband is provided by a filterbank and the noise level is unknown to the monitor.

Also in this direction is the paper "Sequential and Cooperative Sensing for Multichannel Cognitive Radios" by S.J. Kim, and G.B. Giannakis, that formulates the problem of finding the policy that chooses the best time to stop taking measurements and the best set of channels to access for data transmission.

C.-H. Hwang, G.-L. Lai, and S.-C. Chen propose in “Spectrum sensing in wideband OFDM cognitive radios” an OFDM based wideband detector using the idea that a primary user channel appears at a segment of continuous subcarriers. Then in a first step, the maximum likelihood (ML) estimates of the frequency bands are calculated; while in a second step, detection is performed at each suspected band.

In "Invariant wideband spectrum sensing under unknown variances” A. Taherpour, M. Nasiri-Kenari and S. Gazor propose a white space detector based on the Generalized Likelihood Ratio Test given that a minimum number of subbands is vacant. In their model the level of each frequency subband is provided by the output of a filterbank.

For example in "A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios" Z. Tian and G.B. Giannakis propose a wavelet approach in which the signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that carry the relevant information on the frequency locations and power spectral densities of the subbands. Wavelet transform allows to monitorize simultaneously all the possible bandwidths for each of the channels present in the band.

Y. L. Polo, Y. Wang, A. Pandharipande and G. Leus propose in "Compressive wide-band spectrum sensing" a spectrum reconstruction process based on the autocorrelation of a compressed version of the received signal (however it is not clear to me how a non-stationary compressed signal can have an autocorrelation) and assuming sparsity in the

The work presented in "Compressive Detection for Wide-band Spectrum Sensing" by V. Havary-Nassab, S. Hassan and S. Valaee proposes a wideband detector based on a set of random overlapping filters: the energies at the filter outputs are used as compressed measurements to reconstruct the signal energy in each channel.

From arXiv we have "Compressive Wideband Spectrum Sensing for Fixed Frequency Spectrum Allocation" and "Robust Compressive Wideband Spectrum Sensing with Sampling Distortion" by Y. Liu and Q. Wan, where the authors propose an ad-hoc compressed sampling architecture based on the

I will finish with this post with some shameless self-promotion commenting two of my conference publications related to wideband spectrum sensing: "Wideband Spectrum Sensing in Cognitive Radio: Joint Estimation of Noise Variance and Multiple Signal Levels" and "Wideband Spectral Estimation from Compressed Measurements Exploiting Spectral a priori Information in Cognitive Radio Systems" that can be found in the publications section of my homepage. While the first proposes a ML reconstruction of the spectrum when the spectral shape of the primary transmission is assumed known

Here I did not try to be exhaustive in the enumeration of all the existing publications on

**multichannel spectrum sensing**for cognitive radio. The problem is that when the whole bandwidth to monitor is large, sequential individual sensing of many primary channels may not be feasible due to speed constraints. It is here where wideband spectrum sensing comes into play: the bandwidth of interest can be downconverted, digitized and processed directly by the spectral monitor. I will try here to sumarize some recent publications on this topic.**Wideband spectrum sensing**can be described as a complex topic not only because of its technical difficulty but also due to the large number of problems that appear at each of the steps of the detection/estimation procedure. In fact research efforts in multichannel monitoring range from the low level physical sensing to higher level resource allocation problems. That is the reason I prefer to classify the works in this area in terms of the level of the problem they address instead of using the classical division on energy, cyclostationarity or matched filter detection.## Network level: cooperation among nodes.

In "Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity" J.A. Bazerque and G.B. Giannakis propose an architecture in which the estimation algorithm, based on the sparsity of the primary signals in both fequency and space, is distributed among the nodes and converges to the spectrum reconstruction given by a centralized compressive sampling implementation.Similarly A. Taherpour, S. Gazor, and M. Nasiri-Kenari propose in "Wideband spectrum sensing in unknown white Gaussian noise” a distributed network of secondary users that collaborate in order to detect multiple primary users under the assumption that the level of each frequency–domain subband is provided by a filterbank and the noise level is unknown to the monitor.

## Node level: sensing resources scheduling.

Some of the works presented this year at the ICASSP fit under the topic of sensing resources scheduling, such as "Two-stage Spectrum Sensing for Cognitive Radios", "Two-stage Spectrum Detection in Cognitive Radio Networks" of "Adaptive Spectrum Sensing for agile Cognitive Radios" already commented in the post about the sensing session at the ICASSP.Also in this direction is the paper "Sequential and Cooperative Sensing for Multichannel Cognitive Radios" by S.J. Kim, and G.B. Giannakis, that formulates the problem of finding the policy that chooses the best time to stop taking measurements and the best set of channels to access for data transmission.

## Node level: sensing algorithms.

More in the algoritmic direction Z. Quan, S. Cui, A.H. Sayed, and H.V. Poor propose in "Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks" a multiband joint detector formulated as a class of convex optimization problem that maximize the aggregated opportunistic throughput.C.-H. Hwang, G.-L. Lai, and S.-C. Chen propose in “Spectrum sensing in wideband OFDM cognitive radios” an OFDM based wideband detector using the idea that a primary user channel appears at a segment of continuous subcarriers. Then in a first step, the maximum likelihood (ML) estimates of the frequency bands are calculated; while in a second step, detection is performed at each suspected band.

In "Invariant wideband spectrum sensing under unknown variances” A. Taherpour, M. Nasiri-Kenari and S. Gazor propose a white space detector based on the Generalized Likelihood Ratio Test given that a minimum number of subbands is vacant. In their model the level of each frequency subband is provided by the output of a filterbank.

## Physical node level: sensing paradigms.

In wideband spectral sensing the large bandwidth involved makes Nyquist-rate monitoring impractical, due to power consumption and analog implementation complexity constraints. Different sensing paradigms try to deal with this problem:For example in "A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios" Z. Tian and G.B. Giannakis propose a wavelet approach in which the signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that carry the relevant information on the frequency locations and power spectral densities of the subbands. Wavelet transform allows to monitorize simultaneously all the possible bandwidths for each of the channels present in the band.

Y. L. Polo, Y. Wang, A. Pandharipande and G. Leus propose in "Compressive wide-band spectrum sensing" a spectrum reconstruction process based on the autocorrelation of a compressed version of the received signal (however it is not clear to me how a non-stationary compressed signal can have an autocorrelation) and assuming sparsity in the

*spectral edges domain*. They use then the reconstructed spectrum for detecting signal occupancy. A distributed version of this approach is presented in "Distributed Compressive Wide-Band Spectrum Sensing" by Y. Wang, A. Pandharipande, Y. L. Polo and G. Leusy.The work presented in "Compressive Detection for Wide-band Spectrum Sensing" by V. Havary-Nassab, S. Hassan and S. Valaee proposes a wideband detector based on a set of random overlapping filters: the energies at the filter outputs are used as compressed measurements to reconstruct the signal energy in each channel.

From arXiv we have "Compressive Wideband Spectrum Sensing for Fixed Frequency Spectrum Allocation" and "Robust Compressive Wideband Spectrum Sensing with Sampling Distortion" by Y. Liu and Q. Wan, where the authors propose an ad-hoc compressed sampling architecture based on the

*a priori*knowledge of the frequency spectrum allocation of primary radios. The second paper is about the various non-ideal physical effects that in practice appear in the Analog to Information Converter, modeled here as a bounded additive noise.I will finish with this post with some shameless self-promotion commenting two of my conference publications related to wideband spectrum sensing: "Wideband Spectrum Sensing in Cognitive Radio: Joint Estimation of Noise Variance and Multiple Signal Levels" and "Wideband Spectral Estimation from Compressed Measurements Exploiting Spectral a priori Information in Cognitive Radio Systems" that can be found in the publications section of my homepage. While the first proposes a ML reconstruction of the spectrum when the spectral shape of the primary transmission is assumed known

*a priori*, the second studies a similar setup when only a compressed version of the input signal is available to the spectrum monitor (and only a subset of the channels are occupied).Here I did not try to be exhaustive in the enumeration of all the existing publications on

**wideband spectrum sensing**. Instead I gave a general view of some of the research directions within this topic. If you find any missing paper or topic it would be nice to let me know with a comment on this post or an email.Labels: cognitive radio, publications, research, sensing, wideband

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