Blind Equalization Algorithm for Underwater Acoustic Channel Based on Support Vector Regression

Guo-long, Zhang; Yang, Ling; Chen, Liang; Zhao, Bin; Li, Yuan; Wei, Wanjun

doi:10.1109/ihmsc.2019.10134

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…SVM has been applied in the underwater acoustics field, but has mainly focused on underwater target classification, channel equalization, and so on [59,60]. In terms of time series prediction, it is extensively reported in [61].…”

Section: Algorithm 4 Krls Prediction Algorithmmentioning

confidence: 99%

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Liu,

Ma,

Wang

et al. 2024

Remote Sensing

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Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.

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Section: Algorithm 4 Krls Prediction Algorithmmentioning

confidence: 99%

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Liu,

Ma,

Wang

et al. 2024

Remote Sensing

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“…Reference [30] proposed a support vector regression (SVR)‐based batch equalization algorithm called SVR‐MMA. This algorithm is represented as an SVR problem within the machine‐learning framework [31], and it uses data block to converge the channel output signals.…”

Section: Proposed Algorithm 2: Mcc‐bcmmamentioning

confidence: 99%

Adaptive blind equalization for multi‐level QAM signals in impulsive noise environment

et al. 2022

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A maximum correntropy criterion-blind clustering multi-modulus algorithm (MCC-BCMMA) is proposed to equalize multi-level quadrature amplitude modulation (QAM) channels in impulsive noise environment. The novel cost function is based on the maximum correntropy criterion (MCC), theoretical analysis shows that this criterion is useful when the noise is impulsive. Moreover, this cost function is decomposed into in-phase term and orthogonal term to eliminate the phase ambiguity, treats high-order QAM signal to classical QAM4 signal to reduce the error, and calculates the weight coefficients by a simple way. Simulation results show that the newly proposed algorithm MCC-BCMMA can be used to equalize multi-level QAM signals in both Gaussian and impulsive noise environments, and it shows better equalization performance under impulsive noise. The relationship between the kernel size of MCC, the convergence speed and accuracy is also analysed.

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“…Building upon this, they introduced a Chebyshev orthogonal polynomial cascaded FLNN for non-linear channel equalization (Zhao and Zhang, 2008) and an adaptive DFE based on the combination of the FIR and FLNN (Zhao et al, 2011). Moreover, Convolutional Neural Network , Recurrent Neural Networks (Kechriotis et al, 1994;Chagra et al, 2005;Xiao et al, 2008;Zhao et al, 2010;Li et al, 2021;Qiao et al, 2022), Fuzzy Neural Networks (Heng et al, 2006;Chang and Ho, 2009;Chang and Ho, 2011), Extreme Learning Machines (Yang et al, 2018;Liu et al, 2019), Wavelet Neural Networks (Xiao and Dong, 2015), Support Vector Machines (Zhang et al, 2019a), other neural network models and Deep Reinforcement Learning have been employed for channel equalization.…”

Section: Introductionmentioning

confidence: 99%

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

Zhou,

Zhang,

et al. 2024

Front. Mar. Sci.

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IntroductionIn shallow-water environments, the reliability of underwater communication links is often compromised by significant multipath effects. Some equalization techniques such as decision feedback equalizer, and deep neural network equalizer suffer from slow convergence and high computational complexity.MethodsTo address this challenge, this paper proposes a simplified decision feedback Chebyshev function link neural network equalizer (SDF-CFLNNE). The structure of the SDF-CFLNNE employs Chebyshev polynomial function expansion modules to directly and non-linearly transform the input signals into the output layer, without the inclusion of hidden layers. Additionally, it feeds the decision signal back to the input layer rather than the function expansion module, which significantly reduces computational complexity. Considering that, in the training phase of neural networks, the random initialization of weights and biases can substantially impact the training process and the ultimate performance of the network, this paper proposes a chaotic sparrow search algorithm combining the osprey optimization algorithm and Cauchy mutation (OCCSSA) to optimize the initial weights and thresholds of the proposed equalizer. The OCCSSA utilizes the Piecewise chaotic population initialization and combines the exploration strategy of the ospreywith the Cauchy mutation strategy to enhance both global and local search capabilities. RseultsSimulations were conducted using underwater multipath signals generated by the Bellhop Acoustic Toolbox. The results demonstrate that the performance of the SDFCFLNNE initialized by OCCSSA surpasses that of CFLNN-based and traditional nonlinear equalizers, with a notable improvement of 2-6 dB in terms of signal-to-noise ratio at a bit error rate (BER) of 10−4 and a reduced mean square error (MSE). Furthermore, the effectiveness of the proposed equalizer was validated using the lake experimental data, demonstrating lower BER and MSE with improved stability. DiscussionThis underscores thepromise of employing the SDFCFLNNE initialized by OCCSSA as a promising solution to enhance the robustness of underwater communication in challenging environments.

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Blind Equalization Algorithm for Underwater Acoustic Channel Based on Support Vector Regression

Cited by 4 publications

References 6 publications

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms

Adaptive blind equalization for multi‐level QAM signals in impulsive noise environment

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

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