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 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.

Design of Digital Communication System of XCTD Based on FSK

2014 ◽  
Vol 644-650 ◽  
pp. 3972-3975
Author(s):  
Jian Rui Zhao ◽  
Ning Liu ◽  
Yu Hua Wu
Keyword(s):  
Communication System ◽  
Digital Communication ◽  
Signal Transmission ◽  
Digital Signal ◽  
Single Chip ◽  
Modulation And Demodulation ◽  
Waveform Distortion ◽  
Shift Keying ◽  
Digital Communication System ◽  
Temperature Depth

According to the problem of expendable conductivity temperature depth profile (XCTD) of transmission signal waveform distortion and undistinguishable information caused by cable time-varying parameters and influence of seawater. This paper designed a based on FSK (Frequency shift keying) of digital communication system. It used single-chip modem chip FX604 used in FSK modulation and demodulation of the signal. And it implements the digital signal transmission on thin wire communication on the water. The system overcomes unfavorable conditions about transmission channel and marine environment. The circuit is easy and practical.

scholarly journals Spike initiation properties in the axon support high-fidelity signal transmission

2021 ◽  
Author(s):  
Mohammad Amin Kamaleddin ◽  
Nooshin Abdollahi ◽  
Stephanie Ratte ◽  
Steven A Prescott
Keyword(s):  
Pyramidal Neurons ◽  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Digital Signal ◽  
Axial Current ◽  
High Fidelity ◽  
Pass Filter ◽  
Analog To Digital ◽  
Spike Initiation ◽  
High Pass

The axon initial segment (AIS) converts graded depolarization into all-or-none spikes that are transmitted by the axon to downstream neurons. Analog-to-digital transduction and digital signal transmission call for distinct spike initiation properties (filters) and those filters should, therefore, differ between the AIS and distal axon. Here we show that unlike the AIS, which spikes repetitively during sustained depolarization, the axon spikes transiently and only if depolarization reaches threshold before KV1 channels activate. Rate of depolarization is critical. This was shown by optogenetically evoking spikes in the distal axon of CA1 pyramidal neurons using different photostimulus waveforms and pharmacological conditions while recording antidromically propagated spikes at the soma, thus circumventing the prohibitive difficulty of patching intact axons. Computational modeling shows that KV1 channels in the axon implement a high-pass filter that is matched to the axial current waveform associated with spike propagation, thus maximizing the signal-to-noise ratio to ensure high-fidelity transmission of spike-based signals.

scholarly journals Pseudorandom Sequences in Spread-Spectrum Communications Generated by Cellular Automata

2012 ◽  
Vol 10 (5) ◽  
Author(s):  
F.C. Ordaz-Salazar ◽  
J.S. González-Salas ◽  
E. Campos-Cantón ◽  
H.C. Rosu
Keyword(s):  
Cellular Automata ◽  
Spread Spectrum ◽  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Pseudorandom Sequence ◽  
Pseudorandom Sequences ◽  
Information Signal ◽  
Shift Keying ◽  
Golden Code ◽  
Digital Communication Systems

Dynamical systems methods have been recently used in spread-spectrum digital communication systems. Theexpansion of the spectrum using a pseudorandom sequence with a higher frequency than the information signal is thekey feature for its robustness against the signal traveling interference through the channel. In this work, we propose togenerate pseudorandom sequences by employing cellular automata and we check these sequences have thenecessary properties which are required in modern communication systems. The computed sequences obtained bythe cellular automata are tested in a quadrature phase shift keying (QPSK) spread-spectrum communication system.The efficiency of the system is analyzed by computing the bit error rate under different signal to noise ratio conditions.These results are compared with systems that employ Golden code and other typical pseudorandom sequences.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

scholarly journals Imbalanced post- and extrasynaptic SHANK2A functions during development affect social behavior in SHANK2-mediated neuropsychiatric disorders

2021 ◽  
Author(s):  
Ahmed Eltokhi ◽  
Miguel A. Gonzalez-Lozano ◽  
Lars-Lennart Oettl ◽  
Andrey Rozov ◽  
Claudia Pitzer ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Social Interaction ◽  
Social Behavior ◽  
Ampa Receptors ◽  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Neuropsychiatric Disorders ◽  
Autism Spectrum ◽  
Strong Impact ◽  
Odor Processing

AbstractMutations in SHANK genes play an undisputed role in neuropsychiatric disorders. Until now, research has focused on the postsynaptic function of SHANKs, and prominent postsynaptic alterations in glutamatergic signal transmission have been reported in Shank KO mouse models. Recent studies have also suggested a possible presynaptic function of SHANK proteins, but these remain poorly defined. In this study, we examined how SHANK2 can mediate electrophysiological, molecular, and behavioral effects by conditionally overexpressing either wild-type SHANK2A or the extrasynaptic SHANK2A(R462X) variant. SHANK2A overexpression affected pre- and postsynaptic targets and revealed a reversible, development-dependent autism spectrum disorder-like behavior. SHANK2A also mediated redistribution of Ca2+-permeable AMPA receptors between apical and basal hippocampal CA1 dendrites, leading to impaired synaptic plasticity in the basal dendrites. Moreover, SHANK2A overexpression reduced social interaction and increased the excitatory noise in the olfactory cortex during odor processing. In contrast, overexpression of the extrasynaptic SHANK2A(R462X) variant did not impair hippocampal synaptic plasticity, but still altered the expression of presynaptic/axonal signaling proteins. We also observed an attention-deficit/hyperactivity-like behavior and improved social interaction along with enhanced signal-to-noise ratio in cortical odor processing. Our results suggest that the disruption of pre- and postsynaptic SHANK2 functions caused by SHANK2 mutations has a strong impact on social behavior. These findings indicate that pre- and postsynaptic SHANK2 actions cooperate for normal neuronal function, and that an imbalance between these functions may lead to different neuropsychiatric disorders.

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