Nearly-optimal compression matrices for signal power estimation

Daniel Romero; Roberto Lopez-Valcarce

doi:10.1109/SPAWC.2014.6941849

Nearly-optimal compression matrices for signal power estimation

Daniel Romero, Roberto Lopez-Valcarce

Electrical and Computer Engineering

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

We present designs for compression matrices minimizing the Cramér-Rao bound for estimating the power of a stationary Gaussian process, whose second-order statistics are known up to a scaling factor, in the presence of (possibly colored) Gaussian noise. For known noise power, optimum designs can be found assuming either low or high signal-to-noise ratio (SNR). In both cases the optimal schemes sample the frequency bins with highest SNR, suggesting near-optimality for all SNR values. In the case of unknown noise power, optimal patterns in both SNR regimes sample two subsets of frequency bins with lowest and highest SNR, which also suggests that they are nearly-optimal for all SNR values.

Original language	English (US)
Pages	434-438
Number of pages	5
DOIs	https://doi.org/10.1109/SPAWC.2014.6941849
State	Published - Oct 31 2014
Event	2014 15th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2014 - Toronto, Canada Duration: Jun 22 2014 → Jun 25 2014

Other

Other	2014 15th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2014
Country/Territory	Canada
City	Toronto
Period	6/22/14 → 6/25/14

Keywords

Compressive covariance sensing
power estimation
sampler design
spectrum sensing

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AB - We present designs for compression matrices minimizing the Cramér-Rao bound for estimating the power of a stationary Gaussian process, whose second-order statistics are known up to a scaling factor, in the presence of (possibly colored) Gaussian noise. For known noise power, optimum designs can be found assuming either low or high signal-to-noise ratio (SNR). In both cases the optimal schemes sample the frequency bins with highest SNR, suggesting near-optimality for all SNR values. In the case of unknown noise power, optimal patterns in both SNR regimes sample two subsets of frequency bins with lowest and highest SNR, which also suggests that they are nearly-optimal for all SNR values.

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Nearly-optimal compression matrices for signal power estimation

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