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

Abstract

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.

A Hybrid Cognitive Transceiver Architecture: Sensing-Throughput Tradeoff

“…3 and 4. The SNR of the received PU signal can be estimated with the help of various power spectrum and SNR estimation techniques [14,[18][19][20][21][22]. Subsequently, based on the estimated PU SNR, let us denote by γ est , the sensing unit takes a decision about the presence or absence of the PU and the decision can trigger the power control block directly in order to transmit with full power in its data transmission slot if the noise only hypothesis (H 0 ) is selected.…”

Compressive Sparsity Order Estimation for Wideband Cognitive Radio Receiver

“…In this aspect, we consider an eigenvalue-based approach using the eigenvalues of the CS measurement vector in order to estimate the sparsity order of the wideband spectrum using Random Matrix Theory (RMT). In our previous works [17][18][19], the eigenvalue-based approach has been used for Signal to Noise Ratio (SNR) estimation in various noise/channel correlated scenarios. Our proposed method requires no prior information about the PU signals neither the knowledge of channel nor the noise covariance.…”

Compressive SNR estimation for wideband cognitive radio under correlated scenarios

“…In the next step, the R transform of the density of the eigenvalues of the product ofR and ΘR 1 is calculated using (21) and (11). Then the R transform of pRΘR 1 in the second term of (8) becomes pR pRΘR1 (pz).…”

“…It should be noted that the SNR estimation techniques proposed under the assumption of the white noise and uncorrelated channel may not perform well in the presence of noise/channel correlation. In this context, the contribution [7] proposes an eigenvalue-based method to estimate the SNR of the PU signals in the presence of noise correlation and the contribution [11] considers the case of channel correlation considering a single radio channel. However, in practical scenarios, the CRs need to detect and acquire information about a wide spectrum band in order to utilize the spectrum efficiently.…”