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About this book

This book constitutes the joint refereed proceedings of the 20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020, and the 13th Conference on Internet of Things and Smart Spaces, ruSMART 2020. The conference was held virtually due to the COVID-19 pandemic.

The 79 revised full papers presented were carefully reviewed and selected from 225 submissions. The papers of NEW2AN address various aspects of next-generation data networks, with special attention to advanced wireless networking and applications. In particular, they deal with novel and innovative approaches to performance and efficiency analysis of 5G and beyond systems, employed game-theoretical formulations, advanced queuing theory, and stochastic geometry, while also covering the Internet of Things, cyber security, optics, signal processing, as well as business aspects. ruSMART 2020, provides a forum for academic and industrial researchers to discuss new ideas and trends in the emerging areas.

Table of Contents

Frontmatter

New Generation of Smart Services

Frontmatter

Identification of Abnormal Functioning of Devices of Cyber-Physical Systems

This paper undertakes the task of determining the information security state of autonomous objects using information obtained through a side acoustic channel. The basic prerequisites for using externally independent monitoring systems to monitor the state of objects at risk of the influence of threats to information security are considered. An experiment to study the functioning parameters of unmanned vehicles in various functioning situations was performed. The appearance and statistical characteristics of the signals, which enable the identification of abnormal deviations during the operation of unmanned vehicles, are shown. Furthermore, an algorithm of two- and three-class classification of the states of the studied objects is presented. It was found that analysis based on the obtained sample is acutely sensitive to any changes in the software and hardware configuration. Simultaneously, with a minimum time of accumulation of statistical information using the proposed approach based on a given threshold, it becomes possible to determine the point at which the attack was begun. The proposed approach model implies the possibility of using various mathematical apparatus, statistical methods, and machine learning to achieve specified indicators for assessing the state of an object’s information security.

V. V. Semenov, M. E. Sukhoparov, I. S. Lebedev

Energy-Aware Algorithm for LoRa Technology: Prototype Implementation

Internet of things (IoT) development has already become one of the main directions in the telecommunications and the information and communication system development as a whole. Promising solutions in the communication networks evolution, such as the latest LTE standards, concepts, and solutions for building 5G networks critical, include this component as an integral part of a promising communication network. There are various modern solutions for building IoT networks, and Long-Range Wide Area Network (LoRaWAN) is one of them. LoRaWAN is a technical solution for the physical and partially link network layers. The paper proposes an algorithm for ensuring traffic quality of service, latency, and data loss, as well as to provide an effective way of energy consumption. We impalement a prototype for Long Range (LoRa) based edge computing.

Abdukodir Khakimov, Mohammed Saleh Ali Muthanna, Polovov Mikhail, Ibodulaev Ibodullokhodzha, Ammar Muthanna, Konstantin Samouylov

Methods for Reducing the Amount of Data Transmitted and Stored in IoT Systems

In this paper presented method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can use cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach focuses on sensor processing. Correlation and autocorrelation methods for event detection depending on waveform are described in detail and implementation of endpoint architecture is proposed. Novelty and another feature of this approach is the use of not the full waveform, but their components and processing on the device. This significantly reduces the number of operations and complexity of implementation. Other methods focus on the cloud computing paradigm. The results of the simulation show that at the data transfer rate from the sensor ~10 MSample/s, the proposed method allows you to transmit and store 280 bytes in 70 min instead of 157 GB using the bypass method. Reducing data transfer and storage requirements will simplify and reduce the cost of IoT systems, improve performance, and apply additional precision sensors to provide more accurate data.The solutions are focused on low-power and FPGA/ASIC implementations.

Alexander Anufrienko

Analysis of IDS Alert Correlation Techniques for Attacker Group Recognition in Distributed Systems

Intrusion Detection Systems are widely used for detecting attacks based on network traffic and host events. However, the raw data they provide are not suitable for manual analysis due to information overload problem. Alert Correlation Systems are used for Intrusion Detection System data enhancing. They can reduce false positives, eliminate duplicate entries, correlate events, analyze attacker strategy and find attacker groups. In this paper, we are focused on the latter. Classification of existing algorithms according to the methods they use is performed. Then we analyze the categories for the compliance with the attacker group recognition requirements. We conclude by highlighting opportunities for further dataset analysis and supplementation options.

Artem Pavlov, Natalia Voloshina

Explaining Android Application Authorship Attribution Based on Source Code Analysis

Source code authorship attribution is a process of source code authorship identification based on set of known code samples belonging to the given author. One of practical applications of code attribution is a malware analysis and detection. In the paper we explore attribution of Android applications based on classification of source code data with particular focus on explanation of the role of selected features and their impact on the final classifier decision. The proposed solution uses Local Interpretable Model–Agnostic Explanations (LIME) technique to explain decisions produced by classifiers. We explored this approach on several types of classifiers such as SVM, Random Forrest and neural network and dataset containing applications belonging to more than 20 different authors.

Ivan Murenin, Evgenia Novikova, Roman Ushakov, Ivan Kholod

Next Generation Wired/Wireless Advanced Networks and Systems

Frontmatter

The Method of Forming the Digital Clusters for Fifth and Beyond Telecommunication Networks Structure Based on the Quality of Service

The paper presents the results of an analysis of the impact of requirements on the quality of service and traffic on the structure of the telecommunication networks of the fifth and beyond generations. Given the crucial role of the network in building an effective digital economy, the concept of a digital cluster is introduced. A model is proposed that allows you to choose the size of a digital cluster, taking into account the requirements for the quality of service of subscriber traffic and the distribution of users on the territory. The proposed model allows you to create a network structure of digital clusters, taking into account various requirements and user traffic. A method for the location of service delivery points is also proposed, using the proposed model to select the sizes of digital clusters. The results can be used in modeling and planning telecommunication networks of the fifth and beyond generations.

A. Paramonov, N. Chistova, M. Makolkina, A. Koucheryavy

Analyzing the Effectiveness of Dynamic Network Slicing Procedure in 5G Network by Queuing and Simulation Models

In this paper, we propose three metrics that could be used for accessing the effectiveness of a dynamic Network Slicing. On the one hand re-slicing could result in more adaptive resource allocation for different virtual network operators (VNO), but could arise the signaling overhead. On the other hand an insufficient amount of re-slicing could significantly decrease the quality of service for VNO users, but reduce the signaling delays. Proposed metrics could be used for analyzing the above-mentioned effect. We illustrate the metrics by the simulation model for a simple dynamic network slicing algorithm. We also propose a queuing system approach for analyzing dynamic network slicing for 2 VNOs.

Irina Kochetkova, Anastasia Vlaskina, Sofia Burtseva, Valeria Savich, Jiri Hosek

Space Division Multiple Access Performance Evaluation in Ultra-Dense 5G Networks

The fifth-generation (5G) wireless networks will utilize higher frequency mmWave bands with wider bandwidth to increase system capacity in Ultra-Dense Network (UDN) scenarios. Massive multiple-input multiple-output (mMIMO) and beamforming (BF) technology have attracted much attention to compensate for path-loss at higher frequency bands. Using new protocols and procedures, e.g. spatial beam management, motivates to evaluate 3D Beamforming (3DBF) and Full-Dimensional MIMO (FD-MIMO) performance for Space Division Multiple Access (SDMA) with spatial separation of user equipment (UE) in UDN 5G wireless networks. Intelligent SDMA should take into account UE location and include preliminary positioning procedures to steer the transmitted signal of interest (SOI) toward the desired direction and simultaneously, avoiding signal of no interest (SNOI) transmission or reception from the unwanted direction. The purpose of this work is to evaluate the performance of interference suppression rate (ISR) as a relation of SOI to SNOI levels in 5G UDN using MISO (Multiple Input Single Output) when Base Station (BS) implements Adaptive Beamforming (AB) for two neighboring UE angular and distance separation scenarios. SOI to SNOI rate is evaluated for linear and planar antenna array patterns with several elements and contribute to the development of recommendations for UE separation in 5G UDN for spatial multiplexing and SDMA.

Vadim Davydov, Grigoriy Fokin, Vitaly Lazarev, Sergey Makeev

Analysis of the Response Time Characteristics of the Fog Computing Enabled Real-Time Mobile Applications

Fog computing brings computing infrastructure to the edge of the network. This enables resource-greedy real-time mobile applications by offloading them to the fog, which provides enough computing resources and reduces the response time in comparison with cloud computing-based solutions. In the paper, we analyze the two-parameter offloading mechanism that takes into account both the computing complexity and the data size to be transferred in case of offloading. We derive the cumulative distribution function of the response time in terms of Laplace-Stieltjes Transform. It is used for the analysis of the probability that the response time exceeds a predefined threshold, which is specified based on the type of application.

Eduard Sopin, Zolotous Nikita, Kirill Ageev, Sergey Shorgin

Efficiency of RF Energy Harvesting in Modern Wireless Technologies

The paper investigates the effectiveness of wireless power collection of electromagnetic energy emitted by LPD433 and Wi-Fi transmitters as well as by the base stations of the LTE and 5G mobile network. The legal regulations of the Russian Federation that affect wireless energy collection (sanitary zone range, maximum transmitter power and antenna gain, etc.) have been taken into consideration. The efficiency of options for wireless energy collection at various free and cellular frequencies has been compared according to such parameters as collected power, maximum charging range and energy replenishment coefficient. Based on the results of the comparison, the most promising RF bands which can be used for wireless harvesting in Russia have been proposed.

Sviatoslav Iakimenko, Daria Ikonnikova

Information Security State Analysis of Elements of Industry 4.0 Devices in Information Systems

The authors researched problematic issues of the information security state analysis of elements of Industry 4.0 devices. The type and characteristics of behavioral patterns used for the analysis were demonstrated. The authors conducted an experiment aimed at obtaining statistical information from investigated objects. During the experiment, a sample of signal trace patterns was obtained for the states under consideration. The approach based on the k-means clustering method was considered as the decisive identification rule. The approach to identifying the state of Industry 4.0 devices in information systems based on the processing of digitized sequences was proposed. The overall accuracy of the selected method was found to be 98%. The main limitations of the proposed approach are the need of formalization and selecting data groups for the formation of behavioral patterns. The basic advantage of the proposed approach is the relative ease of its implementation and minimum requirements for computing resources.

Mikhail E. Sukhoparov, Ilya S. Lebedev, Viktor V. Semenov

The State Identification of Industry 4.0 Mechatronic Elements Based on Behavioral Patterns

Problematic questions of the state of the Industry 4.0 mechatronic elements have been considered. The prerequisites determining the need to use external monitoring systems have been revealed. The type and statistical characteristics of behavioral patterns used for the analysis have been demonstrated. The proposed approach to the analysis of the autonomous object state is based on clustering methods and allows for the identification of the current state based on the processing of digitized signal traces. An experiment aimed at obtaining statistical information on various types of movement of a mechatronic device element has been described. The obtained data were processed using the k-means method. The approach to identifying the state of Industry 4.0 mechatronic elements based on the processing of digitized sequences received through external channels has been proposed. At the minimum time of the statistical information accumulation with the use of the proposed approach, it becomes possible to reveal differences in the manoeuvres performed by the object, with the probability close to 0.7. The proposed approach to the signal information processing can be used as an additional independent element for identifying the state of Industry 4.0 mechatronic elements. The approach can be quickly adapted to achieve the specified quality of the probabilistic assessment.

Mikhail E. Sukhoparov, Viktor V. Semenov, Kseniya I. Salakhutdinova, Evelina P. Boitsova, Ilya S. Lebedev

Data Mining Algorithms Parallelization in the Framework of Agent-Oriented Programming

This article describes an approach to parallelization of data mining algorithms in the framework of agent-oriented programming for distributed data processing. The conversion of Naive Bayes algorithm into agent-oriented form for distributed execution and the algorithm implementation in the framework of agent-oriented programming are described as an example.

Aleksey Malov, Ivan Kholod, Sergey Rodionov, Evgenia Novikova

The IoT and Big Data in the Logistics Development. Crude Oil Transportation in the Arctic Zone Case Study

The following contribution illustrates effects and potentials that digitalization has on the supply chain. Supply Chain Management itself is considered to be a tool for visualization, optimization and synchronization of various groups of processes within an enterprise, while its digitalization can potentially increase this influence. Such digital technologies as Big Data and Internet of Things in combination create new possibilities for a closer interlinking of participants as well as wide-ranging potentials for an optimization of supply chain planning and logistics management. Similarly, the paper aims to recognize the impacts of Big Data Analytics on information usage in a corporate and supply chain context as it is imperative for companies’ logistics management to access to up-to-date, accurate, and meaningful information. Moreover, such information becomes extremely relevant when working with information that depends on constantly changing external factors, for example, severe climatic conditions. The study highlights the case of the Arctic zone, where emerging technologies allows to optimize the process of oil transportation.

Igor Ilin, Alexandra Borremans, Stepan Bakhaev

Availability of Emergency Power Supply for Voice Communications of Air Traffic Control System

The reliability of the emergency power supply system for voice communications of air traffic control (ATC) systems is considered, taking into account the common set of backup batteries and their real downtime for maintenance operations. Mathematical dependability model of emergency power supply system for voice communications of ATC system on the base of batteries network is developed.The availability of ATC controller’s activity in the energy emergency for batteries network with common set of standby batteries and their maintenance is defined. The analyses of reliability model of ATC controller’s functions for condition-based maintenance and periodical schedule-based maintenance of batteries of batteries is carried out.

Igor Kabashkin, Vadim Philippov

BotSpot: Deep Learning Classification of Bot Accounts Within Twitter

The openness feature of Twitter allows programs to generate and control Twitter accounts automatically via the Twitter API. These accounts, which are known as “bots”, can automatically perform actions such as tweeting, re-tweeting, following, unfollowing, or direct messaging other accounts, just like real people. They can also conduct malicious tasks such as spreading of fake news, spams, malicious software and other cyber-crimes. In this paper, we introduce a novel bot detection approach using deep learning, with the Multi-layer Perceptron Neural Networks and nine features of a bot account. A web crawler is developed to automatically collect data from public Twitter accounts and build the testing and training datasets, with 860 samples of human and bot accounts. After the initial training is done, the Multi-layer Perceptron Neural Networks achieved an overall accuracy rate of 92%, which proves the performance of the proposed approach.

Christopher Braker, Stavros Shiaeles, Gueltoum Bendiab, Nick Savage, Konstantinos Limniotis

Standardization of Road Quality Assessment by Developing Mobile Applications

A significant increase in the network of roads, achieved in recent years, highlights the issues of proper repair and maintenance. Ensuring the safety of roads, improving their technical level along with the construction of new roads becomes the main task of road organizations. The solution of this problem is the ultimate goal of the road maintenance service activity and should be based on intensification of production and increasing its efficiency and quality. Among the measures aimed at improving production, a special place is occupied by the issues of timely performance of works on current repair and maintenance of highways. In these conditions, it is timely to plan the maintenance and maintenance of roads on the basis of scientifically grounded recommendations.Due to the development of mobile applications, mobile networks, new technologies (include 5G) and communications, the timely identification of problem areas, can be solved through the development of specialized services. The article presents a new approach for improving the standardization management method using mobile applications. The analysis of the impact of mobile applications on standardization and the definition of the mechanism of such interaction is demonstrated.

Yury Klochkov, Antonina Glushkova, Albina Gazizulina, Egor Koldov

Service-Oriented Technology Architecture for Value-Based and Personalized Medicine

The current trends of the healthcare industry are value-based and personalized medicine. The requirements of compliance with the principles of value-based and personalized medicine have to be met in the architecture model of a healthcare organization – from business services to IT systems and the technology architecture. The personalized data collection is a sufficient enabler of a value-based and customer-oriented services. This paper is aimed at identifying the devices that could be used to collect and analyze personalized data, and thereby would enable the principles of value-based and personalized medicine; determining the place of such devices within the enterprise architecture; describing the existing and the target model of the technology architecture of a healthcare organization and its services; designing the architecture model of a healthcare organization that would include such devices. The research is grounded on the enterprise architecture approach which allows to describe different interdependent elements of the enterprise management system in one comprehensive model.

Igor V. Ilin, Vadim V. Korablev, Anastasia I. Levina

SoMIAP: Social Media Images Analysis and Prediction Framework

The personal photos captured and submitted by users on social networks can provide several interesting insights about the user’s location; which is a key indicator of their daily activities. This information is invaluable for security organisations, especially for security monitoring and tracking criminal activities. Hence, we propose in this paper a novel approach for location prediction based on the image analysis of the photos posted on social media. Our approach combines two main methods to perform the image analysis; place and face recognition. The first method is used to determine the location area in the analysed image. The second is used to identify people in the analysed image, by locating a face in the image and comparing it with a dataset of images that have been collected from different social platforms. The effectiveness of the proposed approach is demonstrated through performance analysis and experimental results.

Yonghao Shi, Gueltoum Bendiab, Stavros Shiaeles, Nick Savage

Modeling and Investigation of the Movement of the User of Augmented Reality Service

In recent years, the use of augmented reality (AR) applications in various industries and spheres of human life has increased significantly. With the appearance of new AR devices and the development of technology, the mobility of users has increased and the possibilities of using AR have expanded. This leads to the need to develop new AR user traffic models taking into account the features of its movement. The paper presents the results of an investigation of the movement of the user of augmented reality services. Augmented reality user movement models have been developed and a detailed analysis of the influence of user behavior on AR traffic parameters and the quality of experience of AR services has been carried out. The generalized augmented reality user movement model is based on experimental data and can be used in the future for modeling and implementation of AR services.

Maria Makolkina, Alexander Paramonov

Audio Interval Retrieval Using Convolutional Neural Networks

Modern streaming services are increasingly labeling videos based on their visual or audio content. This typically augments the use of technologies such as AI and ML by allowing to use natural speech for searching by keywords and video descriptions. Prior research has successfully provided a number of solutions for speech to text, in the case of a human speech, but this article aims to investigate possible solutions to retrieve sound events based on a natural language query, and estimate how effective and accurate they are. In this study, we specifically focus on the YamNet, AlexNet, and ResNet-50 pre-trained models to automatically classify audio samples using their respective melspectrograms into a number of predefined classes. The predefined classes can represent sounds associated with actions within a video fragment. Two tests are conducted to evaluate the performance of the models on two separate problems: audio classification and intervals retrieval based on a natural language query. Results show that the benchmarked models are comparable in terms of performance, with YamNet slightly outperforming the other two models. YamNet was able to classify single fixed-size audio samples with 92.7% accuracy and 68.75% precision while its average accuracy on intervals retrieval was 71.62% and precision was 41.95%. The investigated method may be embedded into an automated event marking architecture for streaming services.

Ievgeniia Kuzminykh, Dan Shevchuk, Stavros Shiaeles, Bogdan Ghita

Modeling the NB-IoT Transmission Process with Intermittent Network Availability

Standardized by 3GPP, Narrowband Internet-of-Thing (NB-IoT) technology operating in licensed bands is nowadays widely deployed and utilized for static deployments of IoT communications services. The recent trend to equip large complex inherently nomadic systems such as trains and ships with advanced sensory capabilities call for mobility support in NB-IoT technology. Such systems entering and leaving the NB-IoT coverage periodically could lead to synchronized behavior of sensor nodes resulting in occasional spikes in the number of sensors simultaneously accessing the NB-IoT random access channel. In this study, we develop a model capturing behavior of nomadic systems roaming between coverage of NB-IoT technology. The metrics of interest are mean message transmission delay as well as the message loss probability. Our numerical results illustrate that these metrics are mainly affected by the duration of the outage interval and fraction of time systems spends in outage conditions. At the same time, the loss and delay performance only insignificantly affected by the number of sensors implying that NB-IoT random access procedure may efficiently handle sporadic loads.

Nikita Stepanov, Dmitri Moltchanov, Andrey Turlikov

Evaluation of Routing Protocols for Multi-hop Communication in LPWAN

Nowadays, the low-power long-range networks are required for the Internet of Things applications, in which devices can transmit data over long distances and consume low energy. Low-Power Wide-Area Networks (LPWANs) have emerged to focus on a group of networks providing solutions for long-range communication and saving power consumption. However, most such networks rely on star topology networks, where the end nodes send data directly to the gateway, which leads to an issue when many nodes are far from the gateway. Aiming for no need for additional gateways, we consider multi-hop communication in LPWANs in this paper. Routing protocols are used to help some nodes that can become relay nodes and find the optimal route to forward the data to the gateway. In the simulated LoRa network, we evaluated the routing protocols in considering the delivery latency and packet loss ratio in varying the network size and number of nodes. Also, the results bring an insight into the future design of a multi-hop communication supported in LPWANs.

Van Dai Pham, Duc Tran Le, Ruslan Kirichek

Deep Learning with Long Short-Term Memory for IoT Traffic Prediction

5G network is new wireless mobile communication technology beyond 4G networks. These days, many network applications have been emerged and have led to an enormous amount of network traffic. Numerous studies have been conducted for enhancing the prediction accuracy of network traffic applications. Network traffic management and monitoring require technology for traffic prediction without the need for network operators. It is expected that each of the 5G networks and the Internet of things technologies to spread widely in the next few years. On the practical level, 5G uses the Internet of Things (IoT) for working in high-traffic networks with multiple sensors sending their packets to a destination simultaneously, which is an advantage of IoT applications. 5G presents wide bandwidth, low delay, and extremely high data throughput. Predicting network traffic has a great influence on IoT networks which results in reliable communication. A fully functional 5G network will not occur without artificial intelligence (AI) that can learn and make decisions on its own. Deep learning has been successfully applied to traffic prediction where it promotes traffic predictions via powerful fair representation learning. In this paper, we perform the prediction of IoT traffic in time series using LSTM - deep learning. the prediction accuracy has been evaluated using the RMSE as a merit function and mean absolute percentage error (MAPE).

Ali R. Abdellah, Andrey Koucheryavy

VANET Traffic Prediction Using LSTM with Deep Neural Network Learning

Vehicular ad hoc networks (VANETs) are a promising technology that enables the communication between vehicles on roads. It becomes an emerging topic that integrates the capabilities of new generation wireless networks for vehicles. Network traffic prediction allows Intelligent Transport Systems (ITS) for proactive response to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, there is a need for new techniques that can use the information in the data to provide better results while they can scale and cope with increasing amounts of data and growing cities. In this paper, we purpose (Long Short-Term Memory) LSTM deep learning for the prediction of VANET network traffic. We have trained the models using traffic data collected from the VANET network. The prediction accuracy has been evaluated using RMSE as a merit function and another measure of prediction accuracy is the mean absolute percentage error (MAPE).

Ali R. Abdellah, Andrey Koucheryavy

Modelling Medical Devices with Honeypots

Cyber security is one of the key priorities in the modern digitalised and complex network totality. One of the major domains of interest is the healthcare sector where a cyber incident may cause unprecedented circumstances. In the healthcare domain there are abundant networked systems, software and hardware, which may be vulnerable for a cyber intrusion or incident. For cyber resilience, it is important to know the status of the valuable assets under attention. Sensor information has a significant role for achieving the comprehension of the valuable assets in the cyber domain. While networked medical devices form an important asset group in healthcare environment, one interesting solution to gather sensor information are the honeypots. In this paper, honeypot technology is studied for the healthcare domain. Especially typical characteristics of medical devices are considered from the perspective of modelling the medical devices with honeypots. The technical priorities are studied and concluded with the discovered future research topics.

Jouni Ihanus, Tero Kokkonen

High Density Internet of Things Network Analysis

This article presents results of Internet of things network modeling as an ad hoc network. The main attention is paid to the features of the functioning of such a network in high density conditions, up to 1 device per square meter. The paper presents the results of evaluations of the basic operating conditions, namely, the level of interference from neighboring network nodes and the signal-to-noise ratio. The results obtained show that such a network can remain connected, but construction of relatively long routes is required. The developed models make it possible to express the dependence of the noise level and signal-to-noise ratio on the network density, produced traffic, and transmitter power.

Alexander Paramonov, Evgeny Tonkikh, Andrey Koucheryavy, Tatiana M. Tatarnikova

Dynamic Programming Method for Traffic Distribution in LoRaWAN Network

Internet of things (IoT) allows millions of devices to be connected, measured, monitored to automate processes, and support better decision making. On the other hand, these IoT devices demand cost-effective, long-range, and power-efficient sensors and actuators. LoRaWAN can be considered as a promising way to overcome these issues. By leveraging LoRaWAN protocol into IoT systems, it will help to optimize energy consumption, capacity, cost, and coverage of the system. This study proposes a scheme for modeling the LoRAWAN network based on the availability of several independent radio frequency channels. As part of this approach, we also offer a model and calculation method. Numerical results show the efficacy of our proposed scheme.

Mohammed Saleh Ali Muthanna, Ping Wang, Min Wei, Waleed Al-mughalles, Ahsan Rafiq

Study of the Accuracy of Determining the Location of Radio Emission Sources with Complex Signals When Using Autocorrelation and Matrix Receivers in Broadband Tools for Analyzing the Electronic Environment

The article presents a study of the accuracy of determining the location of radio emission sources when using autocorrelation and matrix receivers in the spectrum management systems for the reception and processing of complex broadband signals. The ratio of the root mean square errors of determining the location of the radio emission sources using the autocorrelation and matrix receivers is calculated. In addition, the article establishes the feasibility of using an autocorrelation and matrix receivers for various methods for determining the position of a radio emission source. Results of the studies are presented.

Vladimir P. Likhachev, Alexey S. Podstrigaev, Nguyen Trong Nhan, Vadim V. Davydov, Nikita S. Myazin

Electromagnetic Compatibility Research of Non-standard LTE-1800 TDD Base Stations for Railway Applications and Base Stations LTE-1800 FDD

The subject of this article is the study of electromagnetic compatibility (EMC) conditions of LTE-1800 TDD base stations for railway technological communication in a non-standardized frequency band 1785–1805 MHz and LTE-1800 FDD base stations of traditional mobile operator networks for sharing utilization in adjacent band. Research actuality is due to the future deployment of railway technological communication network based on LTE-1800 TDD with the mobile operator networks of LTE-1800 FDD operating in contiguous frequency bands 1710–1785 MHz/1805–1880 MHz and the needs to requirements for sharing spectrum in common location places of such base stations. Provided result of the study allowed to solve EMC problem based on calculated norms of spatial - territorial spacing between LTE-1800 TDD and LTE-1800 FDD base stations.

Sergey Terentyev, Mikhail Shelkovnikov, Valery Tikhvinskiy, Evgeny Deviatkin

Elimination of Carrier Frequency Offset of Local Oscillator to Improve Accuracy of GNSS Positioning

The article proposes a technique for algorithmic estimation and compensation the inaccuracy of the tuning local oscillator in GNSS chip to the carrier frequency. With this technique the carrier tracking loop provides more accurate calculations of integrated Doppler. Then the code tracking loop including carrier aiding provides more accurate code synchronization. As the result the accuracy of GNSS positioning improves. We observed the records of GPS signals with precise zero carrier frequency offset (CFO) and synthetically included nonzero CFO to them. The using of the proposed technique provided almost the same accuracy of positioning as with precise zero CFO.

Igor Petrov, Aleksandr Gelgor, Timur Lavrukhin

Modified Direct Positioning Method in Satellite Geolocation

The paper presents features of direct positioning method application in satellite geolocation for radio source with unknown signal waveform. This method is used in terrestrial radio monitoring systems along with the widespread two-step method. The paper proposes a modification of the satellite geolocation direct method. The modified direct method reduces computational costs for implementation of signal processing on the satellite geolocation ground station and increases the radio source positioning accuracy. The accuracy benefit of the modified direct method increases when attenuation increases in the channels of adjacent satellites of the satellite geolocation system and signal to noise ratio decreases. The paper analyzes the efficiency of the direct method and its modification with an arbitrary ratio between the frequency band of the radio source signal and the analyzed frequency band of the satellite geolocation ground station. This analysis showed the advantage of the modified direct method in a wide range of analysis bandwidths, signal-to-noise ratios, and attenuation in the adjacent channels.

Pavel Kistanov, Elizaveta Shcherbinina, Alexander Titov, Oleg Tsarik, Igor Tsikin

Offset Generation and Interlayer Network Architecture for 5GNR LDPC Parallel Layered Decoder with Variable Lifting Factor Support

Technical specification of 5G New Radio uses QC-LDPC code as forward error correction coding scheme in data channel for eMBB scenario. The specification gives tough requirements of performance, latency and throughput in addition to code rate flexibility. In this paper we propose a modified parallel layered decoder architecture that is able to support all the mentioned in-standard codeblock lengths in a natural and extendible way. Also we provide estimates on the hardware resources consumption and decoder throughput.

Aleksei Krylov, Andrey Rashich, Chao Zhang, Kewu Peng

Bitstreams Multiplexing with Trellis-Coded Modulation and the Fixed Point LDPC Decoding Procedure for Centralised Wireless Networks

This article is a continuation of the study of enhanced Mobile BroadBand and Ultra-Reliable and Low Latency communication multiplexing methods in the downlink of fifth-generation wireless networks. Based on a previous study, the following approach to multiplexing eMBB and URLLC data streams based on Trellis-Coded Modulation using LDPC codes for fifth generation networks in soft decision mode using different quantization levels is proposed.

Igor A. Pastushok, Nikita A. Boikov, Nikita A. Yankovskii

About Burst Decoding for Block-Permutation LDPC Codes

Hard-decision decoders are considered for burst error correction for low-density parity-check codes. The decoder for block-permutation construction of low-density parity-check proposed. Experiments on complexity and error probability are conducted with burst lengths both within and beyond the burst error correction capability. Also simulation results for Gilbert model are presented.

Andrei Ovchinnikov, Anna Fominykh

Estimation of the Possibility of PAPR Reduction for Optimal Signals

The method of optimizing single frequency signals with a predetermined reduction rate of the level of the energy spectrum and restrictions on the energy and peak-to-average power ratio (PAPR) is presented in this paper. The method of numerical solution of the optimization problem is shown. The effect of restrictions on the solution of the optimization problem is considered. Obtained signals provide the gain in PAPR up to 4.3 dB compared to the case without corresponding restriction.

Aleksandr V. Zhila, Anna S. Ovsyannikova, Sergey V. Zavjalov

Study of Detection Characteristics in Recognition of Simple Radio Pulses and Signals with LFM and PSK in the Autocorrelation Receiver

The article offers both method and device for signal detection with recognition of modulation type. Using simulation methods, the detection characteristics of the developed device in case of simple and complex signals are obtained. Complex signals are signals with linear-frequency modulation or binary phase-shift keying. In addition, the article compares the sensitivity of the device when detecting and recognizing signals of various types.

Nguyen Trong Nhan, Alexey S. Podstrigaev, Vladimir P. Likhachev, Alexey A. Veselkov, Vadim V. Davydov, Nikita S. Myazin, Sergey S. Makeev

Wideband Tunable Delay Line for Microwave Signals Based on RF Photonic Components

The principle of operation of the wideband tunable delay line for microwave signals is presented. Delay is performed after the transfer of the microwave signal to the optical frequency domain. The configuration of the delay line model is described. Bandwidth, losses, and distortion characteristics of the delay line model are experimentally determined.

Alexey S. Podstrigaev, Alexander S. Lukiyanov, Alina A. Galichina, Alexander P. Lavrov, Mikhail V. Parfenov

BER Performance of SEFDM Signals in LTE Fading Channels with Imperfect Channel Knowledge

The paper considers the BER performance of Spectral Efficient Frequency-Division Multiplexing (SEFDM) signals in LTE fading channels with imperfect channel knowledge. Analysis of the effect of channel estimation errors on the BER performance is carried out regardless of the selected estimation method. Demodulation is done using ZF-equalizer and trellis demodulator. The analysis is done for perfect synchronization and perfect knowledge of noise power.

Valentin Salnikov, Andrey Rashich, Viet Them Nguyen, Wei Xue

Sensitivity of Energy Spectrum Shape and BER to Variation of Parameters Used in Constraint on Correlation Coefficient During FTN Pulse Optimization

Optimal signals, the shape of which is the result of solving an optimization problem, may provide high spectral efficiency without significant energy losses. The parameters of optimization constraints such as the value of the correlation coefficient and the number of signals considered in the constraint on bit error rate (BER) performance define the shape of the energy spectrum of signals and their correlation properties. In this work sensitivity of energy spectrum and BER performance to variation of parameters of the constraint on correlation coefficient during optimization of FTN pulse shape according to the criterion of maximum energy concentration within occupied frequency bandwidth is estimated. It is shown that the lower the correlation coefficient, the less the energy spectrum changes when the number of signals considered in the constraint on BER performance varies. At the same time, for a rather high value of correlation coefficient increasing the number of signals considered in the constraint on BER performance leads to widening of occupied frequency bandwidth by 7% and reducing the level of emissions by 40 dB at a fixed frequency offset. Besides, energy gain may reach 7 dB. The results obtained during this research allow numerically estimating the possibilities of achieving high spectral and energy characteristics at transmission rates higher than the Nyquist limit.

Anna S. Ovsyannikova, Sergey V. Zavjalov, Sergey B. Makarov, Ilya I. Lavrenyuk, Xue Wei

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