scholarly journals Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules

2020 ◽  
Vol 10 (23) ◽  
pp. 8380
Author(s):  
Laialy Darwesh ◽  
Natan Kopeika
Keyword(s):  
Deep Learning ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Optical Power ◽  
Detection Methods ◽  
Free Space Optical ◽  
Strong Turbulence ◽  
Energy Efficiency Improvement ◽  
Mmse Estimator

Free space optical communication (FSO) is widely deployed to transmit high data rates for rapid communication traffic increase. Asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation is a very efficient FSO communication technique in terms of transmitted optical power. However, its performance is limited by atmospheric turbulence. When the channel includes strong turbulence or is non-deterministic, the bit error rate (BER) increases. To reach optimal performance, the ACO-OFDM decoder needs to know accurate channel state information (CSI). We propose novel detection using different deep learning (DL) algorithms. Our DL models are compared with minimum mean square error (MMSE) detection methods in different turbulent channels and improve performance especially for non-stationary and non-deterministic channels. Our models yield performance very close to that of the MMSE estimator when the channel is characterized by weak or strong turbulence and is stationary. However, when the channel is non-stationary and variable, our DL model succeeds in improving the performance of the system and decreasing the signal to noise ratio (SNR) by more than 8 dB compared to that of the MMSE estimator, and it succeeds in recovering the received data without needing to know accurate CSI. Our DL decoders also show notable speed and energy efficiency improvement.

scholarly journals Design and performance analysis of spectral-efficient hybrid CPDM-CO-OFDM FSO communication system under diverse weather conditions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruhin Chowdhury ◽  
A. K. M. Sharoar Jahan Choyon
Keyword(s):  
Performance Analysis ◽  
Communication System ◽  
Orthogonal Frequency Division Multiplexing ◽  
Channel Model ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Weather Conditions ◽  
Multipath Fading ◽  
Optical Signal ◽  
Free Space Optical

Abstract A comprehensive design is proposed for the free-space optical (FSO) communication system by hybridizing circular polarization division multiplexing (CPDM) with coherent optical orthogonal frequency division multiplexing (CO-OFDM) and its performance is investigated realistically under diverse turbulent weather conditions of Bangladesh. Here, we consider Gamma–Gamma distribution for the turbulent FSO channel model. Moreover, the proposed scheme presents an excellent performance since the CPDM technique not only maximizes the link capacity of the FSO system but also enhances the spectral efficiency of the system. Besides, multipath fading, which is appeared during the FSO transmission, is significantly mitigated by OFDM modulation. The outcomes from the simulation confirm the advantages of the proposed hybrid scheme and also it can serve as a reference for the FSO application even in turbulent weather conditions. Performance analysis of the proposed model is described in terms of the optical power spectrum, optical signal-to-noise ratio, bit error rate, Q factor, constellation diagrams, and eye diagrams.

scholarly journals Utilization of MIMO Concept for Optical Communication System under Fog Condition

2019 ◽  
Vol 17 (2) ◽  
pp. 130-135
Author(s):  
Farouk Shakir ◽  
Mazin Ali A. Ali ◽  
Firas Ameer
Keyword(s):  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Cost Effective ◽  
Free Space Optical ◽  
Single Input Single Output ◽  
High Data ◽  
Single Output ◽  
Received Power ◽  
Single Input

Free-space optical (FSO) communication consider license free, high data rate, wide bandwidth and cost-effective. Multi-input Multi-output (MIMO) systems can be employed to reduce the attenuation by heavy fog and improve FSO channel capacity. In this paper a single-input single-output and multi–input multi-output examined to investigate the performance of these systems under heavy fog. A comparison is made in terms of received optical power, signal to noise ratio, and bit error rate (BER) using OptiSystem version 7.0. The signal reaches to link up to 1.7km, 1.55km, 1.5km, and 1.4km for 4Tx/4Rx, 3Tx/3Rx, 2Tx/2Rx, 1Tx/1Rxrespectively. The results showed that the quality of received power is enhancement by using up to four beams.

Statistical evaluation of the performance of free-space-optical link affected by optical turbulence with gamma-gamma distribution through time series generated by simulation

2021 ◽  
Vol 67 (1 Jan-Feb) ◽  
pp. 146
Author(s):  
J. A. Lopez-Leyva ◽  
A. Arvizu-Mondragon ◽  
J. Santos-Aguilar ◽  
F. J. Mendieta-Jimenez
Keyword(s):  
Time Series ◽  
Gamma Distribution ◽  
Statistical Evaluation ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Practical Implementation ◽  
Free Space Optical ◽  
Optical Turbulence ◽  
The Mean ◽  
The Impact

In this article, the statistical evaluation of the performance of FSO links subject to dynamic fluctuations of atmospheric optical turbulence that affect the instantaneous value of the received optical power is presented. We reproduce this temporal domain effect with time series generated by simulation considering the optical turbulence as a stochastic process with Gamma-Gamma probability distribution. Also, a phase screen was used in order to observe the impact that optical turbulence has over the optical information field's spatial phase. With our simulations, it is possible to get the two most essential performance parameters required for the practical implementation of FSO links. We obtained the mean signal-to-noise ratio (SNR) and the mean bit error rate (BER) of FSO links affected by optical turbulence with Gamma-Gamma distribution.  The methodology presented in this paper may be readily used to design and implement real-world FSO links.

CO-OFDM System with 16-QAM Subcarrier Modulation Using Reconfigurable Optical Add Drop Multiplexer

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Aruna Rani ◽  
Manjit Singh Bhamrah ◽  
Sanjeev Dewra
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Optical Switch ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Optical Signal ◽  
Signal Power ◽  
Frequency Division ◽  
Link Length ◽  
Transmission Distance ◽  
Fiber Link

AbstractIn this paper, sixteen 50 GHz spaced orthogonal frequency division multiplexing channels at 10 Gbps has been investigated with reconfigurable optical add drop multiplexer based on digital optical switch. The effects of fiber link length and input optical signal power on bit error rate, quality factor, output signal power, optical signal to noise ratio at the receiving side are observed. It is observed that maximal transmission distance of 2,100 km is achieved with an input optical power of −8 dBm.

scholarly journals Theoretical performance of reiterated LMMSE filtering and coded radar waveforms

2009 ◽  
Vol 1 (4) ◽  
pp. 353-359
Author(s):  
Mayazzurra Ruggiano ◽  
Emiel Stolp ◽  
Piet van Genderen
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Sidelobe Suppression ◽  
Radar Systems ◽  
Filter Output ◽  
Radar Waveforms ◽  
Filtering Technique ◽  
Theoretical Performance

Orthogonal frequency division multiplexing (OFDM) waveforms offer strong advantages for integrated communication and radar systems. However, they exhibit inherent high-range sidelobes after matched filtering when standard communication constellation symbols are used for the coding of the carriers. Consequently, they require filtering at the receiver that can serve for sidelobe suppression in order to avoid target masking. However, unmasking is not the only concern; it is crucial to evaluate the filtering scheme both in terms of sidelobe suppression capability and in terms of output signal-to-noise ratio. This last criterion is essential when aiming at also detecting weaker reflections. In this paper the theoretical performance of the reiterated filtering technique based on linear minimum mean square error (LMMSE) is derived and compared to the matched filter. The unmasking capabilities are relevant, but also output power figures. Complex-valued filter output peaks are also evaluated and compared to the matched filter output peaks. Moreover, the performance of reiterated LMMSE is evaluated for OFDM communication-encoded radar waveforms.

Empirical Evaluation of 4 QAM and 4 PSK in OFDM-based Inter-Satellite Communication System

2019 ◽  
Vol 40 (2) ◽  
pp. 143-147 ◽  
Author(s):  
Sushank Chaudhary ◽  
Rudrakshi Kapoor ◽  
Abhishek Sharma
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Satellite Communication ◽  
Signal To Noise Ratio ◽  
Empirical Evaluation ◽  
Communication Link ◽  
Frequency Division ◽  
Free Space Optical ◽  
Optical Technology ◽  
Received Power ◽  
Radio Frequency Spectrum

Abstract Inter-satellite communication is a free-space optical technology which is used to establish communication between satellites in space. This work is focused on the transmission of 10 Gbps data over 4,000 km inter-satellite communication link by incorporating orthogonal frequency division multiplexing scheme. Moreover, a comparison of 4 quadrature amplitude modulation and 4 phase shift key encoding scheme is also presented in this work. The performance of proposed system is evaluated in terms of signal-to-noise ratio, total received power, radio-frequency spectrum and constellation diagrams.

scholarly journals On Training Targets for Deep Learning Approaches to Clean Speech Magnitude Spectrum Estimation

2021 ◽  
Author(s):  
Aaron Nicolson ◽  
Kuldip K. Paliwal
Keyword(s):  
Deep Learning ◽  
Speech Enhancement ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Auditory Scene Analysis ◽  
Spectrum Estimation ◽  
Learning Approaches ◽  
Magnitude Spectrum ◽  
Front End ◽  
Asr System

Estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Training targets for deep learning approaches to clean speech MS estimation fall into three categories: computational auditory scene analysis (CASA), MS, and minimum mean-square error (MMSE) estimator training targets. The choice of training target can have a significant impact on speech enhancement/separation and robust ASR performance. Motivated by this, we find which training target produces enhanced/separated speech at the highest quality and intelligibility, and which is best for an ASR front-end. Three different deep neural network (DNN) types and two datasets that include real-world non-stationary and coloured noise sources at multiple SNR levels were used for evaluation. Ten objective measures were employed, including the word error rate (WER) of the Deep Speech ASR system. We find that training targets that estimate the <i>a priori</i> signal-to-noise ratio (SNR) for MMSE estimators produce the highest objective quality scores. Moreover, we find that the gain of MMSE estimators and the ideal amplitude mask (IAM) produce the highest objective intelligibility scores and are most suitable for an ASR front-end.

Design of 320 Gbps hybrid AMI-PDM-WDM FSO link and its performance comparison with traditional models under diverse weather conditions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruhin Chowdhury ◽  
A. K. M. Sharoar Jahan Choyon
Keyword(s):  
Wavelength Division Multiplexing ◽  
Channel Model ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Weather Conditions ◽  
Optical Signal ◽  
Performance Comparison ◽  
Free Space Optical ◽  
Wavelength Division ◽  
Proposed Model

Abstract A comprehensive design is proposed for alternate mark inversion (AMI)-encoded free-space optical (FSO) communication system by hybridizing polarization division multiplexing (PDM) with wavelength division multiplexing (WDM) and its performance is investigated under diverse weather conditions. The WDM transmitter comprises eight channels transmitting 320 Gbps data over the atmospheric turbulent channel considering gamma–gamma (G–G) distribution for the FSO channel model. A PDM-WDM technique not only maximizes the link capacity of the FSO system but also enhances the spectral efficiency (SE) of the system. Besides, the proposed hybrid AMI-PDM-WDM FSO system performance is compared with the traditional AMI-WDM-PDM and AMI-WDM models to demonstrate the advantages of our proposed model for the design of FSO link. It is observed that our proposed hybrid system exhibits excellent performance under diverse weather conditions over the traditional models in terms of Q factor, received optical power, bit error rate (BER), eye diagrams and optical signal-to-noise ratio (OSNR).

scholarly journals Channel Estimation and Signal Detection in OFDM Systems using Deep Learning

2019 ◽  
Vol 8 (12S) ◽  
pp. 176-179
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Signal Detection ◽  
Orthogonal Frequency Division Multiplexing ◽  
Minimum Mean Square Error ◽  
Ofdm Systems ◽  
Simulation Based ◽  
Channel Statistics ◽  
Channel Distortion ◽  
Deep Learning Model

this article presents “channel estimation and signal detection in OFDM systems by using deep learning”. OFDM stands for “Orthogonal Frequency Division Multiplexing”. This paper exploits end to end handling of wireless OFDM channels by deep learning. It is different from the existing OFDM receivers as it estimates the channel state information (CSI) explicitly and then estimated CSI is used to recover the transmitted symbols, thee proposed approach of deep learning implicitly estimates CSI and the transmitted symbols are recovered directly. The online transmitted data is directly recovered by the offline training a deep learning model using simulation based channel statistics generated data for addressing channel distortion. The performance comparable to “minimum mean square error” (MSME) estimator with transmitted symbols is detected by using deep learning based channel distortion. Using fewer number of pilots, omitting cyclic prefix and in the existence of nonlinear clipping noise, the approach of deep learning is more robust as compared to traditional methods.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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