scholarly journals Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication

2021 ◽  
Vol 9 (11) ◽  
pp. 1252
Author(s):  
Yufei Liu ◽  
Feng Zhou ◽  
Gang Qiao ◽  
Yunjiang Zhao ◽  
Guang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Spread Spectrum ◽  
Communication System ◽  
Signal To Noise Ratio ◽  
Cyclic Shift ◽  
Conventional System ◽  
Signal To Noise ◽  
Underwater Acoustic ◽  
Shift Keying

A deep learning-based cyclic shift keying spread spectrum (CSK-SS) underwater acoustic (UWA) communication system is proposed for improving the performance of the conventional system in low signal-to-noise ratio and multipath effects. The proposed deep learning-based system involves the long- and short-term memory (LSTM) architecture-based neural network model as the receiving module of the system. The neural network is fed with the communication signals passing through known channel impulse responses in the offline stage, and then directly used to demodulate the received signal in the online stage to reduce the influence of the above factors. Numerical simulation and actual data results suggest that the deep learning-based CSK-SS UWA communication system is more reliable communication than a conventional system. In particular, the collected experimental data show that after preprocessing, when the communication rate is less than 180 bps, a bit error rate of less than 10−3 can be obtained at a signal-to-noise ratio of −8 dB.

Improved Whale Optimized MLP Neural Network-Based Learning Mechanism for Multiuser Detection in MIMO Communication System

2020 ◽  
Vol 29 (15) ◽  
pp. 2050239
Author(s):  
R. Umamaheswari ◽  
M. Ramya Princess ◽  
P. Nirmal Kumar
Keyword(s):  
Neural Network ◽  
Spread Spectrum ◽  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Digital Signal ◽  
Learning Mechanism ◽  
Whale Optimization ◽  
Shift Keying ◽  
User Data ◽  
Code Division Multiple

Direct-Sequence Code Division Multiple Access (DS-CDMA) is a digital method to spread spectrum modulation for digital signal transmission. We propose to detect signal in DS-CDMA communication using the learning mechanism. Initially, the user signals are spread using the respective pseudo-noise (PN) code where the input signal is multiplied with the code which is then modulated using the quadrature phase shift keying (QPSK) modulator. The modulated signal is then transmitted in a 3G/4G channel considering all types of fading. The transmitted signal is received by the antenna array which is performed by demodulation. We propose to adaptively assign the weights by employing Improved Whale Optimized Multi-Layer Perceptron Neural Network (IWMLP-NN)-based learning mechanism. To design IWMLP-NN, Improved Whale Optimization Algorithm is combined with multilayer perceptron neural network. This is used instead of the normal Multiple Signal Classification (MUSIC) and least mean squares (LMS)/root-mean-square (RMS) algorithms used in beam-forming networks. After assigning weight through IWMLP-NN-based learning mechanism, we de-spread to get the original user data. We have compared our proposed technique with the normal techniques with the help of plots of Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR). We use both the AWGN channel and fading channel for analysis. Experimental results prove that our proposed method achieves better BER performance results even with deep fading.

scholarly journals M-ary Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communications Based on Virtual Time-Reversal Mirror

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3577
Author(s):  
Zhou ◽  
Liu ◽  
Nie ◽  
Yang ◽  
Zhang ◽  
...  
Keyword(s):  
Time Reversal ◽  
Spread Spectrum ◽  
Underwater Acoustic Communications ◽  
Cyclic Shift ◽  
Acoustic Communications ◽  
Underwater Acoustic ◽  
Underwater Acoustic Channels ◽  
Shift Keying ◽  
Virtual Time ◽  
Time Reversal Mirror

Underwater acoustic communications are challenging because channels are complex, and acoustic waves when propagating in the ocean are subjected to a variety of interferences, such as noise, reflections, scattering and so on. Spread spectrum technique thus has been widely used in underwater acoustic communications for its strong anti-interference ability and good confidentiality. Underwater acoustic channels are typical coherent multipath channels, in which the inter-symbol interference seriously affects the performance of underwater acoustic communications. Time-reversal mirror technique utilizes this physical characteristic of underwater acoustic channels to restrain the inter-symbol interference by reconstructing multipath signals and reduce the influence of channel fading by spatial focusing. This paper presents an M-ary cyclic shift keying spread spectrum underwater acoustic communication scheme based on the virtual time-reversal mirror. Compared to the traditional spread spectrum techniques, this method is more robust, for it uses the M-ary cyclic shift keying spread spectrum to improve the communication rate and uses the virtual time-reversal mirror to ensure a low bit error rate. The performance of this method is verified by simulations and pool experiments.

scholarly journals Baby Cry Detection: Deep Learning and Classical Approaches

2019 ◽  
Author(s):  
Rami Cohen ◽  
Dima Ruinskiy ◽  
Janis Zickfeld ◽  
Hans IJzerman ◽  
Yizhar Lavner
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Social Research ◽  
Signal To Noise Ratio ◽  
Support Vector ◽  
Temporal Behavior ◽  
Learning Approaches ◽  
Signal To Noise ◽  
Vector Machines ◽  
Domestic Environments

In this chapter, we compare deep learning and classical approaches for detection of baby cry sounds in various domestic environments under challenging signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network (CNN) architectures for baby cry detection, and compare their performance to that of classical machine-learning approaches, such as logistic regression and support vector machines. In addition to feed-forward CNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized non-symmetric kernels, better results are obtained compared to common CNN architectures.

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