Conference Paper

# Multiantenna spectrum sensing: Detection of spatial correlation among time-series with unknown spectra.

Commun. Eng. Dept., Univ. of Cantabria, Santander, Spain

DOI: 10.1109/ICASSP.2010.5496151 Conference: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, 14-19 March 2010, Sheraton Dallas Hotel, Dallas, Texas, USA Source: DBLP

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**ABSTRACT:**Running a Network Management Protocol is imperative to ensure network connectivity and stability especially in highly dynamic environment of future Cognitive Radio Sensor Networks. Such protocols have to be characterized by their light overhead in terms of energy, communication and implementation. A solution in this respect is hereby proposed to enable a node in a multichannel environment to quickly establish a control channel with neighboring nodes. The channel selection scheme leverages on the strength of both Dedicated Control Channel and Hopping schemes by implementing a simple weighting scheme and maximizing the use of idle listening periods. By identifying local minima nodes, it also has the potentiality of reducing route failure by 70% when utilized as a routing support.2012 International Symposium on Telecommunication Technologies (ISTT); 01/2012 - [Show abstract] [Hide abstract]

**ABSTRACT:**One of the key problems in cognitive radio (CR) is the detection of primary activity in order to determine which parts of the spectrum are available for opportunistic access. In this work, we present a new multiantenna detector which fully exploits the spatial and temporal structure of the signals. In particular, we derive the generalized likelihood ratio test (GLRT) for the problem of detecting a wideband rank-one signal under spatially uncorrelated noise with equal or different power spectral densities. In order to simplify the maximum likelihood (ML) estimation of the unknown parameters, we use the asymptotic likelihood in the frequency domain. Interestingly, for noises with different distributions and under a low SNR approximation, the GLRT is obtained as a function of the largest eigenvalue of the spectral coherence matrix. Finally, the performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over previously proposed approaches.Sensor Array and Multichannel Signal Processing Workshop (SAM), 2010 IEEE; 11/2010 - [Show abstract] [Hide abstract]

**ABSTRACT:**Spectrum sensing is a key component of the cognitive radio paradigm. Primary signals are typically detected with uncalibrated receivers at signal-to-noise ratios (SNRs) well below decodability levels. Multiantenna detectors exploit spatial independence of receiver thermal noise to boost detection performance and robustness. We study the problem of detecting a Gaussian signal with rank- P unknown spatial covariance matrix in spatially uncorrelated Gaussian noise with unknown covariance using multiple antennas. The generalized likelihood ratio test (GLRT) is derived for two scenarios. In the first one, the noises at all antennas are assumed to have the same (unknown) variance, whereas in the second, a generic diagonal noise covariance matrix is allowed in order to accommodate calibration uncertainties in the different antenna frontends. In the latter case, the GLRT statistic must be obtained numerically, for which an efficient method is presented. Furthermore, for asymptotically low SNR, it is shown that the GLRT does admit a closed form, and the resulting detector performs well in practice. Extensions are presented in order to account for unknown temporal correlation in both signal and noise, as well as frequency-selective channels.IEEE Transactions on Signal Processing 09/2011; · 2.81 Impact Factor

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