scholarly journals Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Lu Bai ◽  
Cheng-Xiang Wang ◽  
Jie Huang ◽  
Qian Xu ◽  
Yuqian Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elevation Angle ◽  
Azimuth Angle ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Mimo Channel ◽  
Angle Of Arrival ◽  
Millimetre Wave ◽  
Channel Characteristics ◽  
Mean Square Errors

This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.

scholarly journals Statistical Characteristics of Measured 3-Dimensional MIMO Channel for Outdoor-to-Indoor Scenario in China and New Zealand

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yawei Yu ◽  
Jianhua Zhang ◽  
Mansoor Shafi ◽  
Min Zhang ◽  
Jawad Mirza
Keyword(s):  
New Zealand ◽  
Channel Model ◽  
Elevation Angle ◽  
Statistical Characteristics ◽  
Mimo Channel ◽  
Delay Spread ◽  
Angle Of Arrival ◽  
Power Delay Profile ◽  
3 Dimensional ◽  
Rms Delay Spread

The 3-dimensional (3D) channel model gives a better understanding of statistical characteristics for practical channels than the 2-dimensional (2D) channel model, by taking the elevation domain into consideration. As different organizations and researchers have agreed to a standard 3D channel model, we attempt to measure the 3D channel and determine the parameters of the standard model. In this paper, we present the statistical propagation results of the 3D multiple-input and multiple-output (MIMO) channel measurement campaign performed in China and New Zealand (NZ). The measurements are done for an outdoor-to-indoor (O2I) urban scenario. The dense indoor terminals at different floors in a building form a typical 3D propagation environment. The key parameters of the channel are estimated from the measured channel impulse response (CIR) using the spatial-alternating generalized expectation-maximization (SAGE) algorithm. Till now there is abundant research performed on the azimuth domain; this paper mainly considers the statistical characteristics of the elevation domain. A statistical analysis of 3D MIMO channel results for both China and NZ measurements is presented for the following parameters: power delay profile (PDP), root mean square (rms), delay spread (DS), elevation angle-of-arrival (EAoA) distribution, elevation angle-of-departure (EAoD) distribution, elevation angular spread (AS), and cross-polarization discrimination (XPD).

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals The Impact of Antenna Height on 3D-MIMO Channel: a Ray Launching Based Analysis

2017 ◽  
Author(s):  
Qi Hong ◽  
Jiliang Zhang ◽  
Hui Zheng ◽  
Hao Li ◽  
Haonan Hu ◽  
...  
Keyword(s):  
Channel Model ◽  
Elevation Angle ◽  
Base Station ◽  
Mimo Channel ◽  
Delay Spread ◽  
Three Dimension ◽  
Angle Of Arrival ◽  
3D Mimo ◽  
The Impact ◽  
Ray Launching

Three dimension (3D) Multi-input-multi-output (MIMO) scheme, which exploits another dimension of the spatial resource, is one of the enabling technologies for the next generation mobile communication. As the elevation angle in 3D-MIMO channel model might varies against the height of the base station transmit antenna, it has to be taken into account carefully. In this paper, the impact of antenna height on the channel characteristics of 3D MIMO channel is investigated by using the intelligent ray launching algorithm (IRLA). Three typical street scenarios, i.e., the straight street, the fork road and the cross road, are selected as benchmarks. On the basis of simulations, joint and marginal probability density functions (PDFs) of both the elevation angle of departure (EAoD) and the elevation angle of arrival (EAoA) are obtained. The elevation angle spread (AS) and the delay spread (DS) under various antenna heights are also discussed. Simulation results indicate that the PDFs of EAoD and EAoA vary characteristics under different street scenarios. Moreover, the minimum value of the DS can be achieved when the antenna height is half of the building height.

scholarly journals Analysis of the Hexagonal Split Ring Resonator using Machine Learning by Considering Split Gap as a Prime Factor

2021 ◽  
Author(s):  
Thippesha D ◽  
Pramodh B R
Keyword(s):  
Machine Learning ◽  
Mathematic Model ◽  
Split Ring Resonator ◽  
Machine Learning Algorithms ◽  
Ring Resonators ◽  
Split Ring ◽  
Millimetre Wave ◽  
Resonance Frequencies ◽  
Wave Range ◽  
Prime Elements

The Hexagonal split-ring resonators (HSRR) are one of the prime elements of metamaterial and patch antenna design in the millimetre-wave range. Even though it's widely used there is no particular mathematic model is available for it. This analysis presents the mathematical nature of the relation between split widths, resonance frequencies; reflection (s11) and mutual coupling (s12) by identifying tend of the data with the aid of machine learning algorithms. The predicted relation will help to design efficient metamaterial, antennas and related appliances.

scholarly journals ML-LME: A Plant Growth Situation Analysis Model Using the Hierarchical Effect of Fractal Dimension

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1322
Author(s):  
Xiaohang Ma ◽  
Yongze Wu ◽  
Jingfang Shen ◽  
Lingfeng Duan ◽  
Ying Liu
Keyword(s):  
Machine Learning ◽  
Fractal Dimension ◽  
Fractal Dimensions ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Linear Mixed Effect Model ◽  
Growth Stages ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Rice Growth

Rice plays an essential role in agricultural production as the most significant food crop. Automated supervision in the process of crop growth is the future development direction of agriculture, and it is also a problem that needs to be solved urgently. Productive cultivation, production and research of crops are attributed to increased automation of supervision in the growth. In this article, for the first time, we propose the concept of rice fractal dimension heterogeneity and define it as rice varieties with different fractal dimension values having various correlations between their traits. To make a comprehensive prediction of the rice growth, Machine Learning and Linear Mixed Effect (ML-LME) model is proposed to model and analyze this heterogeneity, which is based on the existing automatic measurement system RAP and introduces statistical characteristics of fractal dimensions as novel features. Machine learning algorithms are applied to distinguish the rice growth stages with a high degree of accuracy and to excavate the heterogeneity of rice fractal dimensions with statistical meaning. According to the information of growth stage and fractal dimension heterogeneity, a precise prediction of key rice phenotype traits can be received by ML-LME using a Linear Mixed Effect model. In this process, the value of the fractal dimension is divided into groups and then rices of different levels are respectively fitted to improve the accuracy of the subsequent prediction, that is, the heterogeneity of the fractal dimension. Afterwards, we apply the model to analyze the rice pot image. The research results show that the ML-LME model, which possesses the hierarchical effect of fractal dimension, performs more excellently in predicting the growth situation of plants than the traditional regression model does. Further comparison confirmed that the model we proposed is the first to consider the hierarchy structure of plant fractal dimension, and that consideration obviously strengthens the model on the ability of variation interpretation and prediction precision.

scholarly journals Recognition of traffic generated by WebRTC communication

2021 ◽  
Vol 1 (1) ◽  
pp. 15-20
Author(s):  
Nadina Ajdinović ◽  
Semina Nurkić ◽  
Jasmina Baraković Husić ◽  
Sabina Baraković
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Wide Range ◽  
Traffic Recognition ◽  
Audio Video ◽  
Future Work

Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results.

scholarly journals A Novel 3D Nonstationary Channel Model Based on the von Mises-Fisher Scattering Distribution

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yuming Bi ◽  
Jianhua Zhang ◽  
Ming Zeng ◽  
Mengmeng Liu ◽  
Xiaodong Xu
Keyword(s):  
Fourier Transform ◽  
High Speed ◽  
Channel Model ◽  
Elevation Angle ◽  
Three Dimensional ◽  
Azimuth Angle ◽  
Angle Of Arrival ◽  
Mean Values ◽  
Physical Channel ◽  
Von Mises

In the last decade, the nonstationary properties of channel models have attracted more and more attention for many scenarios, that is, vehicle-to-vehicle (V2V), mobile-to-mobile (M2M), and high-speed train (HST). However, little research has been done on the real-physical channel model. In this paper, we propose a generalized three-dimensional (3D) nonstationary channel model, in which the scatterers are assumed to be distributed around the transmitter (Tx) and receiver (Rx) on a two-sphere model. By employing the von Mises-Fisher distribution, the mean values of the azimuth angle of departure (AAoD) and elevation angle of departure (EAoD) and the azimuth angle of arrival (AAoA) and elevation angle of arrival (EAoA) are tracked by time-variant (TV) Brownian Markov (BM) motion paths, which ensure the nonstationarity of the proposed channel model. Moreover, the TV autocorrelation function (ACF) and Doppler power spectrum density (DPSD) of the proposed nonstationary channel model are calculated by using signal processing tools, for example, fast Fourier transform (FFT) and short-time Fourier transform (STFT). In addition, the simulation results show that the TV scatterer distribution results in a nonstationary nonisotropic channel model, and the proposed model can be employed to simulate the 3D nonstationary channel model.

scholarly journals Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1421
Author(s):  
Ahmed E. Khorshid ◽  
Ibrahim N. Alquaydheb ◽  
Fadi Kurdahi ◽  
Roger Piqueras Jover ◽  
Ahmed Eltawil
Keyword(s):  
Machine Learning ◽  
Communication Channel ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Measurements ◽  
Channel Characteristics ◽  
Subject Identification ◽  
Identity Based

In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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