About wideband sensing hardware and the winners of the 3rd Smart Radio Challenge

Pretty busy these days: just a couple of hints. Related to the last post about wideband spectrum sensing I found the following paper that presents a wideband spectrum-sensing processor with reduced complexity achieved by taking advantage of the multitap windowing: A Low-Complexity Wideband Spectrum-Sensing Processor with Adaptive Detection Threshold and Sensing Time by Tsung-Han Yu, Oussama Sekkat, Santiago Rodriguez-Parera, Dejan Marković, and Danijela Čabrić. The interesting part is that they have built a prototype of the system and thus multiple practical issues are presented and studied in this paper.

I also would like to comment on the cognitive radio architecture winner of the Smart Radio Challenge. From the press release:

The system consists of portable base stations – each slightly larger than a laptop computer – and mobile communication units. A central command station is able to monitor the positions of all the rescuers in real time – including indoor locations where GPS signals don’t work – and issue instructions for their proper coordination in the rescue operation, even if there’s no existing mobile phone service."

The winner team, iRADIO team from the University of Calgary, consisted of 5 graduate students:

• K Rawat, Team Leader,
• R. Darraji,
• F. Esparza (visiting student from University of Navarra, Spain),
• M. Rawat, and
• A S. Bassam

Top picture: Astrocytes in culture. Blue color from from the astrocytes DNA and red color from the body. Credit: The Beautiful Mind, a spectacular online photo exhibition featuring images of the brain taken by neuroscientists.

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 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.

Smart Radio Challenge

The winners of the SDR Forum's 3rd annual Smart Radio Challenge were announced. The aim of this contest was to show the usefulness of SDR radios for distress applications. To this end the teams should develop a cooperative sensing scheme that allows the maintainance of a database of public safety emitters, including their location, physical layer parameters and which emergency team uses each emitter. The emergency scenario used to evaluate this cognitive radio system is a disaster area after a major earthquake:

An earthquake has occurred centered in a major metropolitan area measuring 10.0 on the Richter scale. Existing communications infrastructure is out, and as emergency medical services, police, fire, state and federal emergency management personnel arrive on the scene from all over the world, they all begin setting up their own communications systems to aid in rescue efforts. As more and more personnel arrive, finding available spectrum becomes a challenge resulting in unintentional interference between communications of various services."

The winners are

• First place:
  University of Calgary
• Second place:
  Tokyo Institute of Technology
• Best Design:
  University of Calgary
• Best Presentation:
  Worcester Polytechnic Institute
• Best Report:
  Worcester Polytechnic Institute

Unfortunately I could't find any information about the spectrum monitoring techniques used by the winner projects. I will come back to this as soon as I can.

Cognitive radio evolution

On the 25th aniversary of the approval of the unlicensed ISM bands that allowed the huge innovation that occurred in wireless communications (Wi-Fi, Bluetooth...) I present here a couple of views on where cognitive radio technology is.

In a recent discussion in the LinkedIn Cognitive Radio group I showed my opinion on the gradual changes we are living:

In my opinion the change will happen gradually and it is not possible for the telecom operators to stop it. Several countries are studying the possibility of opening the white spaces in the tv-band for unlicensed use in a similar way to FCC's proposal. When this happens the value of the licensed spectrum will go down, since service providers will be able to use this virtually cost-free spectrum.

Other people showed some skepticism on how this would happen, given the huge investment by operators in the auctioned spectrum. I agree that the cognitive radio technology is not mature enough in order to allow unlicensed devices to operate in all the spectrum bands.

Related to this we can find a 2008's blog post in which Keith Nolan attempted to place the cognitive radio technology on a Gartner’s Hype Cycle. This cycle refers to the media coverage and expectations that every new technology goes through. Keith concludes that cognitive radio already had gone over the peak of inflated expectations and the expectations on it were going down. This may had happened with respect to the general media, since if we look at the number of technical papers published with the terms "cognitive radio" (source: Google Scholar) we can see that we are yet into a growing phase:

This agrees with the fact that in order to achieve the practical implementation and revenue-generating exploitation, well established theoretical fundamentals are required. Note that for comparison in this figure I also show the number of papers in a newer topic as it is "compressive sensing". Since this is a more general tool that can be used in multiple fields it will soon beat cognitive radio research effort.

The need of further research is shared by David Cleevely. While in a recent interview he showed an optimistic view on the evolution of cognitive radio he also stated that this will happen in a long-term basis:

In the long-term these new technology developments will open up even more opportunities

Similarly, Joseph Mitola III believes that certain challenges remain in order to allow the standarization of cognitive radio systems, such as the semantic description and reasoning of CR scenarios, interactions and elements.

While the use of tv bands for open spectrum access based on geolocation databases is imminent, the extension of this techniques to other bands of the spectrum will only happen in a long-term basis.

David Cleevely about Cognitive Radio

David Cleevely is an entrepreneur who has started and advised several companies, including the spectrum monitoring company CRFS, which has subsequently carried out the first ever UK-wide spectrum monitoring. Recently Cambridge Wireless published an article showing Cleevely's particular view on Cognitive Radio:

Cognitive radio technology is already being used to some extent in 3G and in defence applications, but its potential – to intelligently react and adjust to optimise the available spectrum – has yet to be fully realised."

The main idea here is that cognitive radio technology is nothing new. In fact, one of the reviews I presented in the last post about spectrum sensing, "A survey of spectrum sensing algorithms for cognitive radio applications" by T. Yucek et al., presented an overview on how Spectrum Sensing is performed today in several wireless standards.
The research on cognitive radio is then not focused on inventing something new, but bringing it to its maximum extent.

David Cleevely also makes another interesting observation. Cognitive Radio deployement will reduce spectrum shortage problem, and as a byproduct it will make the spectrum price falling down:

It’s always a trade-off. Operators will choose between deploying more equipment or buying more spectrum. If equipment is cheap, then it’s not worth paying much for extra spectrum. Equipment and transmission are getting cheaper, and technology is becoming more sophisticated. Once regulators stop saying what spectrum can and cannot be used for, there’s no reason to pay a lot for a once in a lifetime opportunity to offer a service. If someone wants too much for spectrum, use what you have got more efficiently by deploying more kit. The spectrum price will then fall."