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May 25, 2011

Spectrum sensing: ICASSP 2011

ICASSP 2011. As I promised at the end of the post about the session on compressed sensing and sparse reconstruction, here my impressions on the papers presented in my session (SPCOM-L2: Spectrum Sensing for Cognitive Radio). This year's ICASSP presentations have been recorded in video and after the talk I was asked to sign a consent form for the recorded material. Then I assume that it (when allowed by the authors) will be made publicly available in the future. Nice!

"DETECTION DIVERSITY OF MULTIANTENNA SPECTRUM SENSORS"; Gonzalo Vazquez-Vilar, Roberto Lopez-Valcarce, University of Vigo, Spain; Ashish Pandharipande, Philips Research, Netherlands

This paper applies to multiantenna spectrum sensing the concept of diversity order first proposed by Daher and Adve in the radar community. This diversity order corresponds to the slope of the average probability of detection vs. SNR curve at the point at which the average probability of detection equals 0.5, and tightly characterizes the minimum operational SNR at which a sensing scheme begins to work "well" and how fast this happens.

"THE NON-BAYESIAN RESTLESS MULTI-ARMED BANDIT: A CASE OF NEAR-LOGARITHMIC REGRET"; Wenhan Dai, Tsinghua University, China; Yi Gai, Bhaskar Krishnamachari, University of Southern California, US; Qing Zhao, University of California, US

Not so much into the topic. The idea is quite similar to the one presented by the authors last year's ICASSP: to gain knowledge of an underlying stochastic process by scheduling the sensing and transmissions. Here the abstract:
"In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are N arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A player seeks to activate K ≥ 1 arms at each time in order to maximize the expected total reward obtained over multiple plays. RMAB is a challenging problem that is known to be PSPACE-hard in general. We consider in this work the even harder non-Bayesian RMAB, in which the parameters of the Markov chain are assumed to be unknown a priori. We develop an original approach to this problem that is applicable when the corresponding Bayesian problem has the structure that, depending on the known parameter values, the optimal solution is one of a prescribed finite set of policies. In such settings, we propose to learn the optimal policy for the non-Bayesian RMAB by employing a suitable meta-policy which treats each policy from this finite set as an arm in a different non-Bayesian multi-armed bandit problem for which a single-arm selection policy is optimal. We demonstrate this approach by developing a novel sensing policy for opportunistic spectrum access over unknown dynamic channels. We prove that our policy achieves near-logarithmic regret (the difference in expected reward compared to a model-aware genie), which leads to the same average reward that can be achieved by the optimal policy under a known model. This is the first such result in the literature for a non-Bayesian RMAB."


This work presents a spectrum sensing scheme for spatially rank-1 primary signals in spatially uncorrelated noise with unequal noise variances across antennas. Unlike in the paper we presented at CIP'10 the method is not based on the GLRT. The proposed method uses the properties of the autocorrelation function of the received signal. Additionally, an asymptotic analysis of the statistic distribution under both hypothesis is provided. At the end of the talk J. Tugnait presented an sketch of the extension of the algorithm to consider spatial correlation between the noise process observed at each of the antennas, similarly to the work by Stoica and Cedervall in ”Detection tests for array processing in unknown correlated noise fields,” 1997, IEEE Trans. Signal Process.

"MULTIANTENNA DETECTION UNDER NOISE UNCERTAINTY AND PRIMARY USER'S SPATIAL STRUCTURE"; David Ramirez, University of Cantabria, Spain; Gonzalo Vazquez-Vilar, Roberto Lopez-Valcarce, University of Vigo, Spain; Javier Vía, Ignacio Santamaría, University of Cantabria, Spain

The model considered in this work is the same as the one of the previous paper except for the fact that now primary user's signal may present a spatial rank larger than 1 and which is assumed known at the receiver. Hence, assuming a generic diagonal noise covariance matrix, the authors propose a GLRT based detection scheme. Although asymptotic in the low SNR regime, the proposed detector offers good performance even for moderate SNR values. This work is part of a journal paper accepted for publication in IEEE Trans. Signal Process.

"TONE DETECTION OF NON-UNIFORMLY UNDERSAMPLED SIGNALS WITH FREQUENCY EXCISION"; André Bourdoux, Sofie Pollin, Antoine Dejonghe, Liesbet Van der Perre, IMEC, Belgium

In this work the authors perform narrowband signal detection from a set of compressed measurements using a modified basis pursuit algorithm. At first I couldn't get the point of this algorithm. However now I think I understand it: the basis pursuit is applied in the spectral domain so that there exists an important leakeage of the signal power that increases the noise floor. Once the largest frequency component is identified, the modied algorithm estimates its corresponding phase before subtracting it in the original domain. Hence in the next iteration the noise floor has been reduced noticeably. A pity I haven't though about the "small detail" of the phases last year.


The last paper of the session also focuses on multiantenna spectrum sensing. The authors compute the GLRT for a general model that comprises several practical scenarios as a special case, namely spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities (all other parameters are assumed unknown and need to be estimated). Nice presentation.

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