Eigenvalue-Based Sensing and SNR Estimation for Cognitive Radio in Presence of Noise Correlation

Sharma, Shree Krishna; Chatzinotas, Symeon; Ottersten, Björn

doi:10.1109/tvt.2013.2260834

Cited by 89 publications

(105 citation statements)

References 39 publications

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“…For the purpose of completeness, we present the Stieltjes transform for an uncorrelated channel and white noise case from [21]. The Stieltjes transform SR Y of the asymptotic distribution of 1 N YY H for this case can be obtained for any z ∈ C by solving the following cubic polymonial [21].…”

Section: B Main Resultsmentioning

confidence: 99%

“…The Stieltjes transform SR Y of the asymptotic distribution of 1 N YY H for this case can be obtained for any z ∈ C by solving the following cubic polymonial [21].…”

Section: B Main Resultsmentioning

confidence: 99%

“…It can be concluded that channel correlation effect is dominant at higher SNR values in comparison to the uncorrelated case. The detailed description on the combined effect of noise and correlation on the SNR estimation performance of the proposed techniques can be found in [21]. …”

Section: Numerical Resultsmentioning

confidence: 99%

“…Let M be the number of receive dimensions in the cognitive receiver. From a system-model point of view, this factor can be considered to be the number of antennas in a multiantenna-based CR receiver and the number of oversampled branches in an oversampling-based sensing model as considered in [6,7,21]. The signal model presented in this paper is generic and can be applicable to most multidimensional scenarios.…”

Section: Signal Modelmentioning

confidence: 99%

“…For this purpose, the asymptotic eigenvalue probability distribution function (a.e.p.d.f) under signal plus noise hypothesis is needed but still missing in the literature. In our previous work [21], the effect of noise correlation on eigenvalue-based SS techniques has been studied and the a.e.p.d.f of the received signal's covariance matrix has been derived in the presence of correlated noise. In this paper, we derive the expression for the a.e.p.d.f.…”

Section: Introductionmentioning

confidence: 99%

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Eigenvalue based SNR estimation for cognitive radio in presence of channel correlation

Sharma

Chatzinotas

Ottersten

2013

2013 IEEE Global Communications Conference (GLOBECOM)

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Abstract-In addition to spectrum sensing capability required by a Cognitive Radio (CR), Signal to Noise Ratio (SNR) estimation of the primary signals is crucial in order to adapt its coverage area dynamically using underlay techniques. Furthermore, in practical scenarios, the fading channel may be correlated due to various causes such as insufficient scattering in the propagation path and antenna mutual coupling. In this context, we consider the SNR estimation problem for a CR in the presence of channel correlation. We study an eigenvaluebased SNR estimation technique for large-scale CR networks using asymptotic Random Matrix Theory (RMT). 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 in the presence of the correlated channel. Then an SNR estimation technique based on the derived a.e.p.d.f. is proposed for PU SNR in the presence of channel correlation and its performance is evaluated in terms of normalized Mean Square Error (MSE). It is shown that the PU SNR can be accurately estimated in the presence of channel correlation using the proposed technique even in low SNR region.

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Section: B Main Resultsmentioning

confidence: 99%

“…The Stieltjes transform SR Y of the asymptotic distribution of 1 N YY H for this case can be obtained for any z ∈ C by solving the following cubic polymonial [21].…”

Section: B Main Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Eigenvalue based SNR estimation for cognitive radio in presence of channel correlation

Sharma

Chatzinotas

Ottersten

2013

2013 IEEE Global Communications Conference (GLOBECOM)

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Modified Bayesian algorithm‐based compressive sampling for wideband spectrum sensing in cognitive radio network using wavelet transform

Nigam¹,

Pawar²,

Sharma³

2020

Int J Communication

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SummaryThis paper presents the implementation of a modified version of Bayesian relevance vector machine (RVM)‐based compressive sensing method on cognitive radio network with wavelet transform for spectrum hole detection. Bayesian compressive sensing is used in this work to deal with the complexity and uncertainty of the process. The dependency of the Bayesian compressive sensing on the knowledge of noise levels in the measurement has been relaxed through the proposed Bayesian RVM‐based compressive sensing algorithm. This technique recovers the wideband signals even with fewer measurements maintaining considerably good accuracy and speed. Wavelet transform is used in this paper to enable the detection of primary user (PU) even in the low regulated transmission from unlicensed user. The advantage of this approach lies in the fact that it enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation study is performed to evaluate the efficacy of the proposed technique over the cognitive radio environment. The performance of the proposed technique is compared with the conventional Bayesian approach on the basis of recovery error, recovery time and covariance to verify its superiority.

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Enhanced information throughput in multiple input multiple output orthogonal frequency division multiplexing based systems using fractional sampling and iterative signal processing

Sharma

Patwary

Abdel-Maguid

2013

Trans. Emerging Tel. Tech.

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Herein, we study the feasibility of enhanced information throughput capability of Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based wireless communication systems in the context of increasing wireless capacity demand. The concept of Fractional Sampling (FS) is exploited for this purpose to take advantage of diversity created due to it. Furthermore, a novel channel adaptive iterative sequence detector is proposed using the FS technique. The performance analysis of the proposed receiver is carried out and a tighter performance bound is derived. It is found that the FS technique can improve Bit Error Rate (BER) performance of MIMO and OFDM based systems provided that the noise samples are uncorrelated up to a certain level of FS rate. Moreover, it is observed that the performance improvement is a non-linear function of the FS rate. Besides this, the simulation results show that the proposed iterative receiver can significantly enhance the information throughput of MIMO-OFDM based wireless communication systems in comparison to conventional non-iterative receivers.

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Eigenvalue-Based Sensing and SNR Estimation for Cognitive Radio in Presence of Noise Correlation

Cited by 89 publications

References 39 publications

Eigenvalue based SNR estimation for cognitive radio in presence of channel correlation

Eigenvalue based SNR estimation for cognitive radio in presence of channel correlation

Modified Bayesian algorithm‐based compressive sampling for wideband spectrum sensing in cognitive radio network using wavelet transform

Enhanced information throughput in multiple input multiple output orthogonal frequency division multiplexing based systems using fractional sampling and iterative signal processing

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