SNR Estimation Based on Metric Normalization Frequency in Viterbi Decoder

Shr, Kai-Ting; Huang, Yuan-Hao

doi:10.1109/lcomm.2011.040711.110028

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…Accordingly, the wireless channel can be well approximated as an additive white Gaussian noise (AWGN) channel. The received signal under an AWGN channel in one symbol interval is expressed as [15] y t ( ) = x t ( )…”

Section: System Modelmentioning

confidence: 99%

Improved signal‐to‐noise ratio estimation algorithm for asymmetric pulse‐shaped signals

Shao

et al. 2015

IET Communications

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. et al. (2 more authors) (2015) Improved signal-to-noise ratio estimation algorithm for asymmetric pulse-shaped signals. IET Communications, 9 (14). 1788 -1792 . ISSN 1751 https://doi.org/10.1049/iet-com.2014.1162 eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. The split-symbol moment estimation (SSME) algorithm is a conventional method to estimate the signal-to-noise ratio (SNR) in communication systems for M-ary phase shift keying signals. However, the conventional SSME is based on the assumption of symmetric pulse-shaping waveforms, and becomes no longer proper when the shaping pulse is asymmetric. In this paper, we propose a modified SSME algorithm for asymmetric pulse-shaped signals, based on an odd-even symbol splitting approach. Simulation results show that the modified SSME outperforms the conventional SSME for both symmetric and asymmetric pulse-shaped signals in terms of SNR estimation accuracy.

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Section: System Modelmentioning

confidence: 99%

Improved signal‐to‐noise ratio estimation algorithm for asymmetric pulse‐shaped signals

Shao

et al. 2015

IET Communications

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“…Literature [15], [16] designed signal transmission system based on applying physical-layer network coding (PNC), of which literature [15] restrained inter-carrier interference generated by carrier frequency offset by utilizing BP-PNC algorithm, thus lowering transmission error rate of BPSK signal. Literature [17], [18] utilized Cramer-Rao low bound (CRLB) for signal-to-noise ratio (SNR) estimation from BPSK modulated signals. Literature [19] estimated channel based on minimum bit error ratio criterion, and above methods lay a theoretical foundation for improving system output SNR and improve estimation accuracy of BPSK signal.…”

Section: Introductionmentioning

confidence: 99%

Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

et al. 2020

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Given that signal is weakened to a certain extent in the process of noise suppression using mainstream method, and that new noise is introduced by signal processing system, causing the decrease of detection performance, to improve the performance of detection to BPSK signal under the condition of strong noise and no prior information, the detection algorithm of BPSK signal of bistable stochastic resonance model based on scale change is proposed in this study. Using classical bistable stochastic resonance (BSR) system, only low-amplitude and low-frequency periodic signal can be processed. Scale change is first made to BSR in this study, verifying that BSR can be applied to high-frequency BPSK signal under high sampling frequency condition, and nonlinear threshold detection system is designed following Neyman-Pearson criterion to deduce and quantitatively show error rate of detector. Besides, complete flow for signal detection was built by taking it as feedback quantity to adjust the system parameters adaptively. Scale change feasibility and applicability of algorithm proposed in this study were verified through simulation experiment, which lays the theoretical basis for the detection of weak BPSK signal under low signal-to-noise ratio (SNR).

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“…In [11], sixth-order statistics based SNR estimation is proposed for the signals with two different amplitude levels. SNR estimation based on metric normalization frequency in Viterbi decoder is discussed in [12]. SNR of orthogonal frequency division multiplexing signal without the knowledge of any pilot sequences is estimated by the use of cyclic prefix in [13].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

Xie

Peng

Yang

2020

Mobile Information Systems

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Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.

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SNR Estimation Based on Metric Normalization Frequency in Viterbi Decoder

Cited by 11 publications

References 10 publications

Improved signal‐to‐noise ratio estimation algorithm for asymmetric pulse‐shaped signals

Improved signal‐to‐noise ratio estimation algorithm for asymmetric pulse‐shaped signals

Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

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