Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications

Zhang, Youwen; Zakharov, Yuriy; Li, Jianghui

doi:10.1109/access.2018.2794455

Cited by 36 publications

(29 citation statements)

References 48 publications

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“…Simulation shows that it provides an achievable BER with a lower SNR. Y Zhang et al 14 propose a channel estimation scheme for underwater MIMO communication based on the soft decision. Experimental results indicate that the channel estimation has greater improvement in performance.…”

Section: Related Workmentioning

confidence: 99%

A novel signal detection algorithm of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time for underwater acoustic networks based on the improved minimum mean square error

Zhao

Wang

Song

et al. 2020

International Journal of Distributed Sensor Networks

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Multiple-input multiple-output is a commonly used technology supporting for high-rate transmission over frequency-selective fading channels with multiple antennas. Vertical-Bell Laboratories Layered Space-Time is a detection method of a multiple-input multiple-output system, which establishes a direct correspondence between antennas and layers. Studies demonstrate that multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time is a meaningful way for underwater acoustic networks of high performance. However, considering the hardware constraints and energy consumption, achieving a trade-off between the bit error ratio and complexity is a crucial issue for underwater acoustic networks of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time systems. This article proposes a novel signal detection algorithm of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time. First, we address the unitary matrix of the underwater acoustic channel by LDLH decomposition. Second, we order the detection sequence based on the permutation matrix. Third, we detail the implementation of interference cancelation and slice processing. Finally, we perform experiments for comparing the bit error ratio, energy consumption, processing delay, and complexity of the proposed algorithm with zero-forcing Vertical-Bell Laboratories Layered Space-Time, minimum mean square error Vertical-Bell Laboratories Layered Space-Time, and maximum likelihood Vertical-Bell Laboratories Layered Space-Time. Results indicate that our algorithm maintains bit error ratio and the processing delay to that of maximum likelihood Vertical-Bell Laboratories Layered Space-Time algorithm. However, it reduces the energy consumption, which achieves a good trade-off between performance and complexity. This work supports on constructing underwater acoustic networks of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time system.

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Section: Related Workmentioning

confidence: 99%

A novel signal detection algorithm of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time for underwater acoustic networks based on the improved minimum mean square error

Zhao

Wang

Song

et al. 2020

International Journal of Distributed Sensor Networks

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“…T HE least mean square (LMS) algorithm is utilized in many applications including system identification, acoustic echo cancellation, active noise control, channel equalization and so on [1]- [5]. The LMS algorithm can exhibit desirable performance in Gaussian noise since it is established on the second-order moment of the error signal.…”

Section: Introductionmentioning

confidence: 99%

An Improved Variable Kernel Width for Maximum Correntropy Criterion Algorithm

Shi

Zhao

Zakharov

2020

IEEE Trans. Circuits Syst. II

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The maximum correntropy criterion (MCC) algorithm has attracted much attention due to its capability of combating impulsive noise. However, its performance depends on choice of the kernel width, which is a hard issue. Several variable kernel width schemes based on various error functions have been proposed to address this problem. Nevertheless, these methods may not provide an optimal kernel width because they do not contain any knowledge of the background noise that actually has influence on the optimization of the kernel width. This paper proposes an improved variable kernel width MCC algorithm, which is derived by minimizing the squared deviation at each iteration. We also design a reset mechanism for the proposed algorithm to improve its tracking capability when the estimated vector encounters a sudden change. Simulations for system identification and echo cancellation scenarios show that the proposed scheme outperforms other variable kernel width algorithms.

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“…Underwater acoustic (UWA) channel features frequencydependent limited bandwidth, long time-varying multipath spread and severe Doppler effect, which pose a great challenge for reliable and effective UWA communications [1]- [7] and networks [8]- [10], leading to relatively low data rates in a range between a few bits/s (bps) to several tens of kbits/s (kbps) and often unsatisfied performance [11]. Generally, single-carrier (SC) modulation schemes with…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel

Zhang

Zakharov

et al. 2019

IEEE Access

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In recent years, deep learning (DL) techniques have shown great potential in wireless communications. Unlike DL-based receivers for time-invariant or slow time-varying channels, we propose a new DL-based receiver for single carrier communication in time-varying underwater acoustic (UWA) channels. Without the off-line training, the proposed receiver alternately works with online training and test modes for accommodating the time variability of UWA channels. Simulation results show a better detection performance achieved by the proposed DL-based receiver and with a considerable reduction in training overhead compared to the traditional channel-estimate (CE)-based decision feedback equalizer (DFE) in simulation scenarios with a measured sound speed profile. The proposed receiver has also been tested by using the data recorded in an experiment in the South China Sea at a communication range of 8 km. The performance of the receiver is evaluated for various training overheads and noise levels. Experimental results demonstrate that the proposed DL-based receiver can achieve error-free transmission for all 288 burst packets with lower training overhead compared to the traditional receiver with a CE-based DFE.

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Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications

Cited by 36 publications

References 48 publications

A novel signal detection algorithm of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time for underwater acoustic networks based on the improved minimum mean square error

A novel signal detection algorithm of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time for underwater acoustic networks based on the improved minimum mean square error

An Improved Variable Kernel Width for Maximum Correntropy Criterion Algorithm

Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel

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