Estimation of the autocorrelation function of complex Gaussian stationary processes by amplitude clipped signals

Abstract-Cognitive radios sense the radio spectrum in order to find underutilized spectrum and then exploit it in an agile manner. Spectrum sensing has to be performed reliably in challenging propagation environments characterized by shadowing and fading effects as well as heavy-tailed noise distributions. In this paper a robust computationally efficient nonparametric cyclic correlation estimator based on the multivariate (spatial) sign function is proposed. Nonparametric statistics provide additional robustness against heavy-tailed noise and when the noise statistics are not fully known. Asymptotic distribution of the spatial sign cyclic correlation estimator under the null hypothesis is established. Tests using constraint on false alarm rate are derived based on the estimated spatial sign cyclic correlation for singleuser and collaborative spectrum sensing by multiple secondary users. Theoretical justification for detecting cyclostationary signals using the spatial sign cyclic correlation is provided. A sequential detection scheme for reducing the average detection time is proposed. Simulation experiments and theoretical results comparing the proposed method with cyclostationary spectrum sensing methods employing the conventional cyclic correlation estimator are presented. Simulations demonstrate the reliable and highly robust performance of the proposed nonparametric spectrum sensing method in both Gaussian and non-Gaussian noise environments.

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“…Then it follows that the normalized complex autocorrelation function of the hard-limited process y(t) = S(x(t)) is given by [31], [32] …”

Section: A Cyclostationarity Of the Spatial Sign Of A Complex Gaussimentioning

confidence: 99%

Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio

Lundén¹,

Kassam

Koivunen³

2010

IEEE Trans. Signal Process.

120

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“…First, we illustrate that the spatial sign nonlinearity preserves the cyclic frequencies due to the symbol frequency for a cyclic prefix OFDM signal, see Lunden et al (2010), Reed (1959), and Jacovitti and Neri (1994) for more detail. The detector is designed for one nonzero cyclic frequency.…”

Section: Simulation Examplesmentioning

confidence: 94%

Modern Nonparametric, Robust and Multivariate Methods

Nordhausen¹,

Taskinen²

2015

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“…where C h is the auto-covariance matrix of the channel h, and C r is the auto-covariance matrix of the quantized signal r. In (14), C r is obtained by the arcsin law [24],…”

Section: B Blmmse Estimatormentioning

confidence: 99%

Channel estimation for spatially/temporally correlated massive MIMO systems with one-bit ADCs

Kim

Choi

2019

J Wireless Com Network

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This paper considers the channel estimation problem for massive multiple-input multiple-output (MIMO) systems that use one-bit analog-to-digital converters (ADCs). Previous channel estimation techniques for massive MIMO using one-bit ADCs are all based on single-shot estimation without exploiting the inherent temporal correlation in wireless channels. In this paper, we propose an adaptive channel estimation technique taking the spatial and temporal correlations into account for massive MIMO with one-bit ADCs. We first use the Bussgang decomposition to linearize the one-bit quantized received signals. Then, we adopt the Kalman filter to estimate the spatially and temporally correlated channels. Since the quantization noise is not Gaussian, we assume the effective noise as a Gaussian noise with the same statistics to apply the Kalman filtering. We also implement the truncated polynomial expansion-based low complexity channel estimator with negligible performance loss. Numerical results reveal that the proposed channel estimators can improve the estimation accuracy significantly by using the spatial and temporal correlations of channels.

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Estimation of the autocorrelation function of complex Gaussian stationary processes by amplitude clipped signals

Cited by 120 publications

References 9 publications

Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio

Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio

Modern Nonparametric, Robust and Multivariate Methods

Channel estimation for spatially/temporally correlated massive MIMO systems with one-bit ADCs

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