Pilot assisted SNR estimation in a non-coherent M-FSK receiver with a carrier frequency offset

Hassan, Syed Ali; Ingram, M.A.

doi:10.1109/icc.2012.6363676

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…We can haveŜ andN by differentiating log-likelihood function in (12) with respect to S and N , exactly in the same way as done for the data aided case in the previous section. We have the following expression…”

Section: A Data Aided Mlementioning

confidence: 96%

“…Authors in [10] designed SNR estimators for a slow fading environment. Carrier frequency offset effects have been taken into consideration while estimating SNR in [11], [12]. However, in all of these works the approach is only valid for a single-input single-output (SISO) system, which is not sufficient for a WSN where multiple links are formed to transmit the data and constitutes the main motivation behind this study.…”

Section: Introductionmentioning

confidence: 99%

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SNR estimation in a non-coherent MFSK receiver with diversity combining

Arshad

Hassan

2014

2014 International Wireless Communications and Mobile Computing Conference (IWCMC)

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In this paper, the problem of signal-to-noise ratio (SNR) estimator design for a single-input multiple-output (SIMO) communication system employing non-coherent M-ary frequency shift keying (NCMFSK) modulation scheme is considered. The transmitted signal undergoes Rayleigh fading and additive white Gaussian noise (AWGN) and is received at a receiver with L diversity branches. Closed-form expressions of data aided (DA) and non-data aided (NDA) estimators have been derived using the maximum likelihood (ML) estimation approach. Cramer-Rao bound has been evaluated to compare the performance of the designed estimators. The effect of increasing the receiver diversity branches on the performance of estimators has been quantified.

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Section: A Data Aided Mlementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

SNR estimation in a non-coherent MFSK receiver with diversity combining

Arshad

Hassan

2014

2014 International Wireless Communications and Mobile Computing Conference (IWCMC)

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“…Techniques that may be used to estimate γ from received data include pilot assisted estimation [15] and various iterative minimum mean square error based techniques [21,3]. A perfect estimate for γ was assumed in this article.…”

Section: Noise Threshold Calculationmentioning

confidence: 99%

Cooperative Prediction for Cognitive Radio Networks

Barnes

Maharaj

Alfa

2016

Wireless Pers Commun

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Combining spectrum sensing (SS) and primary user (PU) traffic forecasting provides a cognitive radio network (CRN) with a platform from which informed and proactive operational decisions can be made. The success of these decisions is largely dependent on prediction accuracy. Allowing secondary users (SU) to perform these predictions in a collaborative manner allows for an improvement in the accuracy of this process, since individual SUs may suffer from SS and prediction inaccuracies due to poor channel conditions. To overcome these problems a collaborative approach to forecasting PU traffic behaviour, that combines SS and forecasting through SU cooperation, has been proposed in this article. Both pre-fusion and post-fusion scenarios for cooperative prediction were investigated and a number of binary prediction methods were considered (including the authors' own simple technique). Cooperative prediction performance was investigated, under various PU traffic conditions, for a group of ten SUs experiencing different channel conditions and a sub-optimal cooperative forecasting algorithm was proposed to minimise cooperative prediction error. Simulation results indicated that the accuracy of the prediction methods was influenced by the PU traffic pattern and that cooperative prediction lead to a significant improvement in prediction accuracy under most of the traffic conditions considered. However, this came at the cost of increased computational complexity. The pre-fusion scenario was found to be the most accurate scenario (up to 25 % improvement), but was also eleven times more complex than when no fusion was employed. The cooperative forecasting algorithm was found to further improve these results.

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Maximum likelihood SNR estimation for non-coherent FSK-based cooperative networks over correlated Rayleigh fading channels

Arshad

Hassan

2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

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Pilot assisted SNR estimation in a non-coherent M-FSK receiver with a carrier frequency offset

Cited by 3 publications

References 9 publications

SNR estimation in a non-coherent MFSK receiver with diversity combining

SNR estimation in a non-coherent MFSK receiver with diversity combining

Cooperative Prediction for Cognitive Radio Networks

Maximum likelihood SNR estimation for non-coherent FSK-based cooperative networks over correlated Rayleigh fading channels

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