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 Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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 Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia

2021 ◽  
Vol 5 (4) ◽  
pp. 544
Author(s):  
Antonius Angga Kurniawan ◽  
Metty Mustikasari
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Data Input ◽  
Fake News ◽  
Accuracy Test ◽  
Learning Techniques ◽  
Data Word ◽  
Wrong Prediction ◽  
Long Short Term Memory ◽  
Better Than

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

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.

Compact UWB–MIMO antenna with quad-band-notched characteristic

2016 ◽  
Vol 9 (5) ◽  
pp. 1147-1153 ◽  
Author(s):  
Ling Wu ◽  
Yingqing Xia
Keyword(s):  
Correlation Coefficient ◽  
Radiation Pattern ◽  
Mimo System ◽  
Ground Plane ◽  
Multiple Input Multiple Output ◽  
Mimo Antenna ◽  
X Band ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Better Than

With quad-band-notched characteristic, a compact ultrawideband (UWB) multiple-input-multiple-output (MIMO) antenna is introduced in the paper. The UWB–MIMO system has two similar monopole elements and occupies 30 × 45 mm2. By inserting two L-shaped slots, CSRR and C-shaped stubs, four notched bands are achieved (3.25–3.9, 5.11–5.35, 5.5–6.06, and 7.18–7.88 GHz) to filter WiMAX, lower WLAN, upper WLAN, and X-band. Meanwhile, the isolation is obviously enhanced with three metal strips on the ground plane. Results indicate that the antenna covers UWB frequency band of 3.1 – 10.6 GHz except four rejected bands, isolation of better than −18 dB, envelope correlation coefficient of <0.02, and good radiation pattern, thus making it useful for UWB systems.

scholarly journals Compact Wideband MIMO Diversity Antenna for Mobile Applications Using Multi-Layered Structure

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1307
Author(s):  
Omer Arabi ◽  
Chan Hwang See ◽  
Atta Ullah ◽  
Nazar Ali ◽  
Bo Liu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Dielectric Constants ◽  
Impedance Bandwidth ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Diversity Antenna ◽  
Better Than ◽  
Envelope Correlation

A closely packed wideband multiple-input multiple-output (MIMO)/diversity antenna (of two ports) with a small size of less than 18.5 mm by 18.5 mm is proposed for mobile communication applications. The antenna can be orthogonally configured for corner installation or by placing it on a back-to-back structure for compact modules. To enhance the isolation and widen the bandwidth, the antenna is structured with multiple layers having differing dielectric constants. The feeding through a via significantly reduces the ground waves. A multi-fidelity surrogate model-assisted design exploration method is employed to obtain the optimized antenna geometric parameters efficiently. The antenna design was investigated using electromagnetic simulation and a physical realization of the optimal design was then created and subjected to a range of tests. The specific parameters investigated included reflection coefficients, mutual coupling between the input ports, radiation patterns, efficiency and parameters specific to MIMO behavior: envelope correlation coefficient and pattern diversity multiplexing coefficient. It was found that the antenna has an impedance bandwidth of approximately 4 GHz, mutual coupling between input ports of better than −18 dB and an envelope correlation coefficient of less than 0.002 across the operating band. This makes it a good candidate design for many mobile MIMO applications.

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 Design of a SIMO Deep Learning-Based Chaos Shift Keying (DLCSK) Communication System

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 333
Author(s):  
Majid Mobini ◽  
Georges Kaddoum ◽  
Marijan Herceg
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Fading Channels ◽  
Communication Systems ◽  
Chaotic Maps ◽  
Short Term Memory ◽  
Reference Signal ◽  
Shift Keying ◽  
Input Multiple Output ◽  
Chaos Shift Keying

This paper brings forward a Deep Learning (DL)-based Chaos Shift Keying (DLCSK) demodulation scheme to promote the capabilities of existing chaos-based wireless communication systems. In coherent Chaos Shift Keying (CSK) schemes, we need synchronization of chaotic sequences, which is still practically impossible in a disturbing environment. Moreover, the conventional Differential Chaos Shift Keying (DCSK) scheme has a drawback, that for each bit, half of the bit duration is spent sending non-information bearing reference samples. To deal with this drawback, a Long Short-Term Memory (LSTM)-based receiver is trained offline, using chaotic maps through a finite number of channel realizations, and then used for classifying online modulated signals. We presented that the proposed receiver can learn different chaotic maps and estimate channels implicitly, and then retrieves the transmitted messages without any need for chaos synchronization or reference signal transmissions. Simulation results for both the AWGN and Rayleigh fading channels show a remarkable BER performance improvement compared to the conventional DCSK scheme. The proposed DLCSK system will provide opportunities for a new class of receivers by leveraging the advantages of DL, such as effective serial and parallel connectivity. A Single Input Multiple Output (SIMO) architecture of the DLCSK receiver with excellent reliability is introduced to show its capabilities. The SIMO DLCSK benefits from a DL-based channel estimation approach, which makes this architecture simpler and more efficient for applications where channel estimation is problematic, such as massive MIMO, mmWave, and cloud-based communication systems.

Compact 4-Port UWB-MIMO Slot Antenna with Dual Polarization and Low Correlation for Spatial Diversity Application

Frequenz ◽  
2018 ◽  
Vol 72 (11-12) ◽  
pp. 503-509
Author(s):  
Rohit Mathur ◽  
Santanu Dwari
Keyword(s):  
Multiple Input Multiple Output ◽  
Wide Band ◽  
Spatial Diversity ◽  
Slot Antenna ◽  
Dual Polarization ◽  
Low Correlation ◽  
Compact Size ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Better Than

Abstract A compact 4-port ultra-wide band (UWB) multiple-input-multiple-output (MIMO) slot antenna with dual polarization is presented. The key features of antenna are: has directive radiation in two planes and low correlation without use of additional decoupling structure. The antenna contains four microstrip feedlines having circular patches backed by stepped circular slots. Orthogonal arrangement of each slot antenna increases compactness with polarization diversity and good isolation. The antenna has compact size of 36×36×0.8 mm3. It operates in the frequency band of 3.1 to 11.9 GHz and isolation is better than 15 dB. The superior diversity performance is ensured by calculating envelope correlation coefficient (ECC) and diversity gain. In addition to guarantee distortion less transmission in UWB group delay is also measured.

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.

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