Cramer-Rao Lower Bound for Non-Data-Aided SNR Estimation of Linear Modulation Schemes

Gappmair, Wilfried

doi:10.1109/tcomm.2008.060275

Cited by 45 publications

(27 citation statements)

References 7 publications

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“…It can be seen that the performance of the NDA ML estimator is very close to the related CRLB. For comparison purposes, the normalized CRLB for DA and NDA estimation of the SNR, indicated as NCRLB DA and NCRLB NDA , is plotted as well [1], [2].…”

Section: A Signals With Constant Envelopementioning

confidence: 99%

“…2 illustrates the evolution of the normalized MSE, which is very close to NCRLB NDA . Also shown is the performance of the DD SNV estimator [1], [2] deviating more and more from NCRLB NDA as soon as SNR ≤ 15 dB. …”

Section: Proceedingsmentioning

confidence: 99%

“…In a recently published paper [1], the Cramer-Rao lower bound (CRLB) for non-data-aided (NDA) estimation of the signal-to-noise ratio (SNR) has been derived, which applies to linear modulation schemes in general. When compared to the jitter performance of NDA algorithms available from the open literature [2], one observes a considerable gap at lower values of the true SNR.…”

Section: Introductionmentioning

confidence: 99%

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ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals

Gappmair

López-Valcarce

Mosquera

2008

2008 6th International Symposium on Communication Systems, Networks and Digital Signal Processing

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The recently published Cramer-Rao lower bound for non-data-aided (NDA) estimation of the signalto-noise ratio (SNR) reveals a considerable gap, when compared to the jitter performance of NDA algorithms available from the open literature. The maximum-likelihood (ML) solution derived in this paper closes this gap. However, the latter provides a set of two nonlinear vector equations, which might be simplified only for modulation schemes with constant envelope like M-ary PSK. For signals with nonconstant envelope, like 16-QAM as most prominent example in this respect, a much less complex approach based on the expectation-maximization (EM) principle is developed in this paper. In the medium SNR range, this bridges part of the performance gap mentioned previously. Over the full SNR range, we propose a hybrid algorithm, where the EM estimate is replaced by a moment-based method as soon as the true SNR drops below a predefined threshold.

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Section: A Signals With Constant Envelopementioning

confidence: 99%

Section: Proceedingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals

Gappmair

López-Valcarce

Mosquera

2008

2008 6th International Symposium on Communication Systems, Networks and Digital Signal Processing

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“…This holds also true for channel parameters, most prominently expressed by estimation of the SNR [2]. It was the rising interest in adaptive schemes during the last decade, which caused that powerful SNR algorithms have been developed for all relevant categories: (a) data-aided or non-data-aided [11,12], depending on whether data symbols are known to the receiver or not; (b) coherent or non-coherent with respect to carrier frequency/ phase [13], depending on the possibility of successful carrier synchronisation; (c) based on baudrate or oversampling [14], depending on whether the symbol timing could be reliably established in advance or not. Governed by powerful error correction schemes such as turbo and LDPC schemes, code-aware parameter estimation was extensively explored over the last decade as well [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Estimation of carrier and channel parameters in time‐selective fading channels

Gappmair

Bergmann

2015

IET Communications

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Assuming a land-mobile satellite link with negligible multipath and shadowing effects, the corresponding channel model is adequately described by a correlated Rician distribution. In this context, the estimation of carrier and channel parameters has been investigated in some earlier work by means of an algorithmic approach primarily based on heuristic considerations. In the current study, the authors will review the results obtained so far from a maximumlikelihood (ML) point of view. A major output of the analysis is that the simple ad-hoc framework achieved previously is indeed an ML solution, when the impairments introduced by Rician fading are spectrally flat and strictly band limited.

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“…Indeed, they are either derived in closed forms or numerically computed [4,5,6,7]. But to the best of our knowledge, they have never been addressed in the case of OFDM transmissions when we are interested in the SNR per subcarrier, taking into account the mutual information between subcarriers.…”

Section: Introductionmentioning

confidence: 99%

Cramer-Rao Bounds for SNR Estimates in Multicarrier Transmissions

Bellili

Stéphenne

Affes

2009

VTC Spring 2009 - IEEE 69th Vehicular Technology Conference

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Considering orthogonal frequency division multiplexing (OFDM) transmissions, we derive analytical expressions for the Cramér-Rao bounds for the subcarrier signal-to-noise ratio (SNR) estimates. The channel coefficients of the different subcarriers are assumed to be constant over the observation interval and the received signal is assumed to be corrupted by additive white Gaussian noise (AWGN). We will show that exploiting the mutual information between the different tones improves the achievable performance of subcarrier SNR estimators.

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Cramer-Rao Lower Bound for Non-Data-Aided SNR Estimation of Linear Modulation Schemes

Cited by 45 publications

References 7 publications

ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals

ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals

Estimation of carrier and channel parameters in time‐selective fading channels

Cramer-Rao Bounds for SNR Estimates in Multicarrier Transmissions

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