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Jul 15, 2010

Cognitive Radio in

Arxiv. Sometimes I wonder how research could be done before the Internet era. I can imagine how long it would take to get something published: after finished, the article had to be submitted by post to the associated editor, then to the reviewers, comments and responses back and forth, back and forth and so on. When the article is finally accepted it would take a couple of months before the research community had access to the printed journal.

While the publishing time got drastically reduced with the invention of the email and electronic documents, it can still be considered slow compared to the current pace of the research. Since this may hinder the interaction between different groups working in the same topic, some researchers choose to submit an early (non peer-reviewed) electronic version of their work to e-print repositories, such as

In this repository there exists no section dedicated exclusively to signal processing articles. Nevertheless, many of them are archived under the Information Theory (cs.IT) tag. If we search for "cognitive radio" we can find some related papers.

Presented at CrownCom'10 last June, the paper "Binary is Good: A Binary Inference Framework for Primary User Separation in Cognitive Radio Networks" by Huy Nguyen, Rong Zheng and Zhu Han poses the problem of distinguishing and characterizing primary users when we have a large number of collaborating secondary users. The observations by secondary users are modeled as boolean OR mixtures of underlying binary vectors. I had not seen before this approach, kind of "binary processing".

In the paper "Spectrum Sensing in Cooperative Cognitive Radio Networks with Partial CSI" Chong Han, Ido Nevat, and Jinhong Yuan develop an algorithm for cooperative spectrum sensing in a relay based cognitive radio network. To this end they use a bayesian expectation maximisation to approximate the solution of the non-convex problem resulting from a simplification of the likelihood. Beats the energy detector. From almost the same authors is the paper "Blind Spectrum Sensing in Cognitive Radio over Fading Channels and Frequency Offsets", which studies the effect of frequency offsets due to oscillator mismatch and Doppler effect. A novel approach to approximate the Likelihood Ratio Test (LRT) using a single point estimate using a low complexity Adaptive Notch Filter (ANF).

Other papers present the key word compressive in their title, such as "Compressive Wideband Spectrum Sensing for Fixed Frequency Spectrum Allocation" and "Robust Compressive Wideband Spectrum Sensing with Sampling Distortion" by Yipeng Liu and Qun Wan. These papers attempt to use compressive sensing techniques to wideband spectrum reconstruction.

However, I want to finish this post with two of Yonina Eldar's papers about the modulated converter entitled "Xampling", which made me discover arXiv.

P.S. Congratulations Spain!

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Jul 9, 2010

What is Cognitive Radio? Different views

Fractal view.Recently Dr. Sudharman K. Jayaweera from the University of New Mexico visited the University of Vigo to present a short course on Cognitive Radio and Dynamic Spectrum Sharing. One of his introductory slides tried to deepen into the definition of Cognitive Radio, that results to be different depending on which area people are working on.

For example, by looking at the landmark paper by Joseph Mitola III we realize that his view stresses the reasoning capabilities of the network. This particular definition was taken mainly by the Computer Science (CS) community.

S. K. Jayaweera: A short course on DSS CR

Jayaweera's slide summarizes the definitions of Cognitive Radio used by different research communities:
  • Hardware community: Cognitive Radio (CR) is essentially an extension of software defined radio (SDR).
  • PHY-layer researchers: Cognitive Radio is synonymous with dynamic spectrum sharing (DSS), that is, just a medium access paradigm.
  • Computer Science view: for computer and IT personell a device/system with machine learning capabilities.
  • Networking community: a device that performs cross-layer optimizations.
  • Information theorists: CR reduces to an interference channel with side information.

That is, while the Cognitive Radio paradigm may include all these concepts each area completely identify CR with their particular interests. It is not clear to me how the definition will evolve as CR becomes a mature technology or which are the capabilities a CR node will include. Probably, as Jayaweera points out, a CR system will include a combination of all mentioned capabilities (and maybe even more).

Credit: The picture on the top is a view of the Sedona National Park through a Sierpinski Tetrahedra. It is part of the photographic Math-Art essays "of a Fractal Nature" by Gayla Chandler.

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Jul 2, 2010

2010 Qualcomm Cognitive Radio Competition

Cognitive Radio.A group of students of the ECE Illinois won the 2010 Qualcomm Cognitive Radio Contest. While the 3rd Smart Radio Challenge consisted in developing an integral framework for detection and tracking of different radios on an emergency scenario, the 2010 Qualcomm Cognitive Radio Competition required students to develop and implement algorithms that detect wireless microphone signals within a spectrum band.

While detecting digital television signals using the ATSC (American standard for digital television broadcasting) is not a big deal (given their embedded pilot tones), detecting 200 KHz wide wireless microphone signals with the detection performance required by the FCC proposal becomes a challenge. Qualcomm provided the different teams with training data, consisting on both signal sets with a wideband of 6 MHz and information on the frequencies where microphone signals were located. Contestants had to use this data to find a fitting model for the microphone signals and develop algorithms to detect them. The algorithm was judged based on its performance, novelty and implementation complexity.

The approaches followed by the 14 participating teams were fairly different. For example, the ECE Virginia Tech team:
"The students developed a fairly robust solution. They determined a baseline noise correlation matrix from a data set with no wireless microphone signals. To see if a signal is present in a new environment, they compare the new correlation matrix with the baseline matrix using singular value decomposition. If their algorithm determines there is a signal, it calculates the center frequency from the measured auto correlation of the signal."

The winning team (ECE Illinois), advised by V. V. Veeravalli, commented that the key problem was learning to distinguish wireless microphone signals from narrowband interference caused by other electrical devices. They addressed this problem by characterizing the unique features of this narrowband interference.

Congratulations to the winners!

Edit: In the CRT blog appears a link (that I missed) to a presentation by Stephen J. Shellhammer about this contest. Since it includes some technical details I decided to include it here.

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