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SNR Estimation for Multi-dimensional Cognitive Receiver under Correlated Channel/Noise
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.ORCID iD: 0000-0003-2298-6774
2013 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 12, no 12, 6392-6405 p.Article in journal (Refereed) Published
Abstract [en]

In addition to Spectrum Sensing (SS) capability required by a Cognitive Radio (CR), Signal to Noise Ratio (SNR) estimation of the primary signals at the CR receiver is crucial in order to adapt its coverage area dynamically using underlay techniques. In practical scenarios, channel and noise may be correlated due to various reasons and SNR estimation techniques with the assumption of white noise and uncorrelated channel may not be suitable for estimating the primary SNR. In this paper, firstly, we study the performance of different eigenvalue-based SS techniques in the presence of channel or/and noise correlation. Secondly, we carry out detailed theoretical analysis of the signal plus noise hypothesis to derive the asymptotic eigenvalue probability distribution function (a.e.p.d.f.) of the received signal's covariance matrix under the following two cases: (i) correlated channel and white noise, and (ii) correlated channel and correlated noise, which is the main contribution of this paper. Finally, an SNR estimation technique based on the derived a.e.p.d.f is proposed in the presence of channel/noise correlation and its performance is evaluated in terms of normalized Mean Square Error (MSE). It is shown that the PU SNR can be reliably estimated when the CR sensing module is aware of the channel/noise correlation.

Place, publisher, year, edition, pages
2013. Vol. 12, no 12, 6392-6405 p.
Keyword [en]
Channel estimation, Correlation, Covariance matrices, Estimation, Sensors, Signal to noise ratio, Cognitive radio (CR), channel/noise correlation, random matrix theory (RMT), signal to noise ratio (SNR) estimation, spectrum sensing (SS)
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-139015DOI: 10.1109/TWC.2013.103113.130523ISI: 000328966200036OAI: diva2:682148

QC 20140611

Available from: 2013-12-23 Created: 2013-12-23 Last updated: 2014-06-11Bibliographically approved

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Ottersten, Björn
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