Equalizer for video rate transmission in multipath underwater communications

Geller, Benoît; Capellano, V.; Brossier, Jean-Marc; Essebbar, A.; Jourdain, Geneviève

doi:10.1109/48.486790

Cited by 38 publications

(19 citation statements)

References 11 publications

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“…Adopt the sparse underwater acoustic channel as that in Ref. [11], the channel function is It is because that at high values of SNR, the main interference to channel estimation comes from the information sequence { ( , not from the white Gaussian noise. It can be seen that the significant improvement in performance is obtained at the first iteration for IST (denoted as IST1) scheme compared with ST scheme; the performance is further improved at the second iteration for IST (denoted as IST2); there is no significant improvement between the second iteration and the third iteration (denoted as IST3), so the convergent performance of IST scheme is very good.…”

Section: Simulation Resultsmentioning

confidence: 99%

Sparse underwater acoustic OFDM channel estimation based on superimposed training

Zhao

Meng

Shilou

2009

J. Marine. Sci. Appl.

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A superimposed training (ST) based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing (OFDM) channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel's properties include large channel dimensions and a sparse structure, so a matching pursuit (MP) algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise (AWGN) to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.

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Section: Simulation Resultsmentioning

confidence: 99%

Sparse underwater acoustic OFDM channel estimation based on superimposed training

Zhao

Meng

Shilou

2009

J. Marine. Sci. Appl.

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“…The step-size µ is chosen so as to insure a global convergence of the algorithm to the optimum delay. An adaptive step-size [34] could be implemented in order to have both a faster convergence and low MSE at the steadystate. However, for simplicity reasons, the step size value is chosen as constant during our synchronization process.…”

Section: Simulation Resultsmentioning

confidence: 99%

Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals

Nasr

Atallah

Cherif

et al. 2018

IEEE Trans. Wireless Commun.

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Abstract-This paper presents an off-line algorithm for dynamical time delay recovery for which the whole observation block is used. The time offset varies over the observation interval following a random walk model. The proposed synchronizer applies to data-aided (DA), non-data-aided (NDA) and code-aided (CA) modes. Theoretical performance of the off-line technique is derived and compared to simulation results. The Bayesian Cramer-Rao Bound (BCRB) is also evaluated for DA, NDA and CA modes and for both the off-line and on-line scenarios. Simulation results show the improvement brought by the off-line and the CA schemes. The presented algorithm outperforms the conventional on-line estimator, which only takes into account the current and previous observations, and its MSE approaches the BCRB.

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“…The step size can be optimized [15] [16] but this is beyond the scope of our paper. In practice, the term ∂z k (u) ∂u u=τk−1 can be obtained by interpolating the sampled version of the received signal and evaluating the derivative of the obtained interpolator at τ k−1 .…”

Section: Soft Timing Recovery Algorithmmentioning

confidence: 99%

On a hybrid preamble/soft-output demapper approach for time synchronization for IEEE 802.15.6 narrowband WBAN

et al. 2015

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International audience1 internet. These wearable networks are based in part on emerging wireless technologies with very low power consumption such as narrow-band systems which were proposed as a part of the IEEE 802.15.6 WBAN standard [1]. A WBAN is operating in a heterogeneous environment where different wireless technologies coexist in the same frequency band. Thus, in order to insure reliable data transmission , time synchronization is a crucial task in a WBAN receiver providing robustness to noise, interference and contention. As time synchronization is processed at front-end, a bad synchronization degrades the whole receiver. Both Data Aided (DA) and Non Data Aided (NDA) time delay estimation techniques have been employed in real systems such as in [2] and [3]. Even if DA techniques lead to the best achievable performance, one needs to look for other solutions not requiring the transmission of a pilot signal in order to enhance the spectral efficiency and to save power. To deal with this problem, NDA time recovery techniques are implemented using only the received signal. Unfortunately, compared to DA techniques , the system performance is degraded especially at low Signal to Noise Ratio (SNR) values. To overcome such disadvantages, with Abstract: In this paper, we present a maximum likelihood (ML) based time synchronization algorithm for Wireless Body Area Networks (WBAN). The proposed technique takes advantage of soft information retrieved from the soft demapper for the time delay estimation. This algorithm has a low complexity and is adapted to the frame structure specified by the IEEE 802.15.6 standard [1] for the narrowband systems. S i m u l a t i o n r e s u l t s h a v e s h o w n g o o d performance which approach the theoretical mean square error limit bound represented by the Cramer Rao Bound (CRB)

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Equalizer for video rate transmission in multipath underwater communications

Abstract: International audienc

Cited by 38 publications

References 11 publications

Sparse underwater acoustic OFDM channel estimation based on superimposed training

Sparse underwater acoustic OFDM channel estimation based on superimposed training

Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals

On a hybrid preamble/soft-output demapper approach for time synchronization for IEEE 802.15.6 narrowband WBAN

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