scholarly journals An Empirical Study of Deep Learning Models for LED Signal Demodulation in Optical Camera Communication

Network ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 261-278
Author(s):  
AbdulHaseeb Ahmed ◽  
Sethuraman Trichy Viswanathan ◽  
MD Rashed Rahman ◽  
Ashwin Ashok
Keyword(s):  
Deep Learning ◽  
Empirical Studies ◽  
Multiple Input Multiple Output ◽  
Empirical Evaluation ◽  
Learning Models ◽  
Led Array ◽  
Input Multiple Output ◽  
Signal Demodulation ◽  
Light Beams ◽  
Optical Camera Communication

Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication.

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

2021 ◽  
pp. 154851292110228
Author(s):  
Ravisankar Malladi ◽  
Manoj Kumar Beuria ◽  
Ravi Shankar ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Deep Learning ◽  
Interference Cancellation ◽  
Multiple Access ◽  
Multiple Input Multiple Output ◽  
Optimal Solution ◽  
Channel State ◽  
Fifth Generation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
And Performance

In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

scholarly journals Empirical Evaluation of Shallow and Deep Learning Classifiers for Arabic Sentiment Analysis

2022 ◽  
Vol 21 (1) ◽  
pp. 1-25
Author(s):  
Ali Bou Nassif ◽  
Abdollah Masoud Darya ◽  
Ashraf Elnagar
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Empirical Evaluation ◽  
Detailed Comparison ◽  
Learning Models ◽  
Similar Work ◽  
Learning Classifiers ◽  
Arabic Sentiment Analysis ◽  
Gated Recurrent Units

This work presents a detailed comparison of the performance of deep learning models such as convolutional neural networks, long short-term memory, gated recurrent units, their hybrids, and a selection of shallow learning classifiers for sentiment analysis of Arabic reviews. Additionally, the comparison includes state-of-the-art models such as the transformer architecture and the araBERT pre-trained model. The datasets used in this study are multi-dialect Arabic hotel and book review datasets, which are some of the largest publicly available datasets for Arabic reviews. Results showed deep learning outperforming shallow learning for binary and multi-label classification, in contrast with the results of similar work reported in the literature. This discrepancy in outcome was caused by dataset size as we found it to be proportional to the performance of deep learning models. The performance of deep and shallow learning techniques was analyzed in terms of accuracy and F1 score. The best performing shallow learning technique was Random Forest followed by Decision Tree, and AdaBoost. The deep learning models performed similarly using a default embedding layer, while the transformer model performed best when augmented with araBERT.

scholarly journals Deep Learning Based Antenna Selection for MIMO SDR System

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6987
Author(s):  
Shida Zhong ◽  
Haogang Feng ◽  
Peichang Zhang ◽  
Jiajun Xu ◽  
Huancong Luo ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Software Defined Radio ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Input Multiple Output ◽  
Set Up ◽  
Time Division

In this paper, we propose and implement a novel framework of deep learning based antenna selection (DLBAS)-aided multiple-input–multiple-output (MIMO) software defined radio (SDR) system. The system is constructed with the following three steps: (1) a MIMO SDR communication platform is first constructed, which is capable of achieving uplink communication from users to the base station via time division duplex (TDD); (2) we use the deep neural network (DNN) from our previous work to construct a deep learning decision server to assist the MIMO SDR platform for making intelligent decision for antenna selection, which transforms the optimization-driven decision making method into a data-driven decision making method; and (3) we set up the deep learning decision server as a multithreading server to improve the resource utilization ratio. To evaluate the performance of the DLBAS-aided MIMO SDR system, a norm-based antenna selection (NBAS) scheme is selected for comparison. The results show that the proposed DLBAS scheme performed equally to the NBAS scheme in real-time and out-performed the MIMO system without AS with up to 53% improvement on average channel capacity gain.

Investigation of Bi-Directional LSTM deep learning-based ubiquitous MIMO uplink NOMA detection for military application considering Robust channel conditions

2021 ◽  
pp. 154851292110504
Author(s):  
Joel Alanya-Beltran ◽  
Ravi Shankar ◽  
Patteti Krishna ◽  
Selva Kumar S
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Multiple Input Multiple Output ◽  
Detector Performance ◽  
Channel Error ◽  
Limited Bandwidth ◽  
Error Factor ◽  
Wireless Users ◽  
Input Multiple Output ◽  
Better Than

Ubiquitous multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks (UMNs) have emerged as an important technology for enabling security and other applications that need continuous monitoring. Their implementation, however, could be obstructed by the limited bandwidth available due to many wireless users. In this paper, bidirectional long short-term memory (LSTM)-based MIMO-NOMA detector is analyzed considering imperfect successive interference cancelation (SIC). Simulation results demonstrate that the traditional SIC MIMO-NOMA scheme achieves 15 dB, and the deep learning (DL) MIMO-NOMA scheme achieves 11 dB for [Formula: see text] number of iterations. There is a gap of 4 dB which means that the DL-based MIMO-NOMA performs better than the traditional SIC MIMO-NOMA techniques. It has been observed that when the channel error factor increases from 0 to 1, the performance of DL decreases significantly. For the channel error factor value less than 0.07, the DL detector performance much better than the SIC detector even though the perfect channel state information (CSI) is considered. The DL detector’s performance decreases significantly where variations between the actual and expected channel states occurred, although the DL-based detectors’ performance was able to sustain its predominance within a specified tolerance range.

scholarly journals Optical Camera Communications for IoT–Rolling-Shutter Based MIMO Scheme with Grouped LED Array Transmitter

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3361 ◽  
Author(s):  
Shivani Rajendra Teli ◽  
Vicente Matus ◽  
Stanislav Zvanovec ◽  
Rafael Perez-Jimenez ◽  
Stanislav Vitek ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Light Emitting Diode ◽  
Multiple Input Multiple Output ◽  
Raspberry Pi ◽  
High Data ◽  
Data Throughput ◽  
Data Rates ◽  
Led Array ◽  
Communication Links ◽  
Input Multiple Output

In optical camera communications (OCC), the provision of both flicker-free illumination and high data rates are challenging issues, which can be addressed by utilizing the rolling-shutter (RS) property of the image sensors as the receiver (Rx). In this paper, we propose an RS-based multiple-input multiple-output OCC scheme for the Internet of things (IoT) application. A simplified design of multi-channel transmitter (Tx) using a 7.2 × 7.2 cm2 small 8 × 8 distributed light emitting diode (LED) array, based on grouping of LEDs, is proposed for flicker-free transmission. We carry out an experimental investigation of the indoor OCC system by employing a Raspberry Pi camera as the Rx, with RS capturing mode. Despite the small area of the display, flicker-free communication links within the range of 20–100 cm are established with data throughput of 960 to 120 bps sufficient for IoT. A method to extend link spans up to 1.8 m and the data throughput to 13.44 kbps using different configurations of multi-channel Tx is provided. The peak signal-to-noise ratio of ~14 and 16 dB and the rate of successfully received bits of 99.4 and 81% are measured for the shutter speeds of 200 and 800 µs for a link span of 1 m, respectively.

scholarly journals Deep double-pilot-based hybrid precoding in UAV-enabled mmWave massive MIMO

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ziyao Hong ◽  
Ting Li ◽  
Fei Li
Keyword(s):  
Deep Learning ◽  
Communication System ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
The Other ◽  
Time Varying ◽  
Hybrid Precoding ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Aerial Vehicle

Abstract Unmanned aerial vehicle (UAV)-enabled communication system provides flexibility and reliability compared to conventional ones. Millimeter wave (mmWave) and massive multiple-input–multiple-output (MIMO) have widely been researched since recent years, which are promising techniques for the next and even the later generation communication system. Hybrid precoding, as a method to reduce the high cost in hardware and power brought by massive antenna array, develops fiercely and is often combined to deep learning, a kind of popular optimization tool, which brings an overwhelming performance. On the other hand, there are not so many attentions about the hybrid precoding in time-varying mmWave massive MIMO, which is necessary to be considered in a UAV-enabled communication scenario because the performance will degrade seriously if the channel changed while the transmitter and receiver use the precoding matrix corresponding to the expired channel, yet. In this paper, we propose a double-pilot-based hybrid precoding system, which completes analog precoding and digital precoding separately—predicting the previous one using deep learning structure and updating equivalent channel frequently for the post one by enhancing the frequency of equivalent channel estimation.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

scholarly journals Deep Learning Based Energy Efficient Scheme for Massive MIMO

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1776-1780
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Energy Efficient ◽  
Massive Mimo ◽  
Multipath Propagation ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Input Multiple Output ◽  
Efficient Scheme ◽  
Output System

This paper proposes a Deep Learning Energy Efficient Scheme (DLEE) for a massive multiple input multiple output system (MIMO). Massive MIMO is deployed using large number of antennas for multiple users. The proposed DLEE, learns the relationship between spatial beamforming pattern and the power consumption in a base station. In this work, we design a novel learning method where the spatial correlation across UE antennas are taken as input feature vector and find the output labels which give us the energy efficiency in a BS. Due to multipath propagation, other methods only try to address the energy efficiency problem through the bit rate and the power required for the throughput to be efficient. This paper discusses the unsupervised algorithm DLEE which is similar to an autoencoder by combining the power consumed due to radiation pattern through beamforming and the DL framework to address the energy efficiency to an extent of 12% in a BS.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

Comparative Analysis of Machine Learning Models for Predictive Control of the Cyclopentadine Production Process

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Samuel V. Saraiva ◽  
Flávio V. Silva ◽  
Frede O. Carvalho
Keyword(s):  
Machine Learning ◽  
Production Process ◽  
Predictive Control ◽  
Control Strategies ◽  
Multiple Input Multiple Output ◽  
Optimum Operating ◽  
Support Vector ◽  
Learning Models ◽  
Input Multiple Output ◽  
Machine Learning Models

AbstractDifferent control strategies have been investigated to improve nonlinear system operations. One such strategy is the use of nonlinear predictive controllers (NMPCs) based on machine learning models. These models, such as artificial neural networks (NN), support vector machines (SVMs), and neuro-fuzzy networks (NF), present satisfactory adaptability to the complexity of the processes. In this aspect, a comparative study of the models in the predictive control of a complex system, such as MIMO (multiple-input-multiple-output) process of the production process of cyclopentadiene, is of interest and is the aim of this work. In this aspect, we find, through simulations, that the NMPCs presented adequate performance, especially those based on an SVM, concerning the servo and regulatory problem scenarios, keeping the process at the optimum operating point, especially for unattainable setpoint. The instability in the use of the classical proportional-integral-derivative linear control is also shown.

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