Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems

Savaux, Vincent; Louët, Yves; Djoko-Kouam, Moïse; Skrzypczak, Alexandre

doi:10.1186/1687-6180-2013-128

Cited by 20 publications

(44 citation statements)

References 33 publications

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“…The authors would like to thank Instituto Nacional de Telecomunicações (Inatel) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for partially funding the work presented in this paper. 1 …”

Section: Acknowledgementsmentioning

confidence: 99%

“…The zoomed curve shows the rangê n A − λ ≤ n ≤n A . In this example, h[0] << h [1] and the threshold criterion points that h [1] should be considered as the first multipath, resulting in a positive offset error.…”

Section: Performance Analysis Of the Synchronization Algorithmmentioning

confidence: 99%

“…Note that the preamble sequence presented in [12], which is designed for OFDM, can be reproduced using the GFDM matrix notation p 1 …”

Section: Synchronization Approach For Gfdmmentioning

confidence: 99%

“…Many of today's digital communication systems use orthogonal frequency division multiplexing (OFDM) [1] as physical layer interface, mainly because of its robustness against frequency-selective channels and easy means of implementation. However, OFDM has some important drawbacks that make it questionable for future wireless systems as 5G networks [2].…”

Section: Introductionmentioning

confidence: 99%

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A synchronization technique for generalized frequency division multiplexing

Gaspar

Mendes

Michailow

et al. 2014

EURASIP J. Adv. Signal Process.

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Generalized frequency division multiplexing (GFDM) is a block filtered multicarrier modulation scheme recently proposed for future wireless communication systems. It generalizes the concept of orthogonal frequency division multiplexing (OFDM), featuring multiple circularly pulse-shaped subsymbols per subcarrier. This paper presents an algorithm for GFDM synchronization and investigates the use of a preamble that consists of two identical parts combined with a windowing process in order to satisfy low out of band radiation requirements. The performance of time and frequency estimation, with and without windowing, is evaluated in terms of the statistical properties of residual offsets and the impact on symbol error rate over frequency-selective channels. A flexible metric that quantifies the penalty of misalignments is derived. The results show that this approach performs practically as state-of-the-art OFDM schemes known in the literature, while it additionally can reduce the sidelobes of the spectrum emission.

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Section: Acknowledgementsmentioning

confidence: 99%

Section: Performance Analysis Of the Synchronization Algorithmmentioning

confidence: 99%

“…Note that the preamble sequence presented in [12], which is designed for OFDM, can be reproduced using the GFDM matrix notation p 1 …”

Section: Synchronization Approach For Gfdmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A synchronization technique for generalized frequency division multiplexing

Gaspar

Mendes

Michailow

et al. 2014

EURASIP J. Adv. Signal Process.

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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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