User-centric wireless access virtualization (WAV) allows each user to be served by a set of carefully selected transmission points (TPs) forming a user-specific virtual base station (uVBS) adapted to its environment and quality-of-service (QoS) requirement. In this way, this new concept breaks away from the conventional cell-centric architecture to provide boundaryless communications in future fifth-generation (5G) networks. This fundamental structural 5G evolution and the ultra-dense multi-tier heterogeneous context foreseen in such networks require an inevitable rethinking of efficient scalable TP clustering. As such, this paper proposes three innovative low-cost clustering approaches that enable the user-centric WAV and provide dynamic, adaptive, and overlapping TP clusters while requiring not only negligible overhead cost but also minimum signaling changes at both network and user sides. Contrary to existing clustering techniques, the new ones we propose better leverage the 5G features such as extreme densification and massive connectivity as well as new concepts such as millimeter wave (mmWave) spectrum and massive multiple-input-multiple-output (MIMO). The simulations show that they may achieve until 154% and 282% of throughput and coverage gains, respectively. Furthermore, these approaches are flexible enough to be adapted to different network dimensions (i.e., space and time), thereby paving the way for achieving the dramatic performance improvements required by the 5G networks to cope with the upcoming mobile data deluge.

User-Centric Base-Station Wireless Access Virtualization for Future 5G Networks

The future 6G and tactile internet (TI) paradigms pose challenges and demand for requirements that are far beyond what the 5G—today at its dawn—will ever achieve. The classical approaches in designing devices, systems and infrastructures will not be suitable to build the AI-driven (Artificial Intelligence) 6G network. This work envisages a critical part for MEMS/NEMS technologies in making 6G turn into reality. Such a leading role sits on a reformulation of the common concept of Hardware (HW) triggered by Micro/Nanotechnologies and Materials. To this end, the WEAF Mnecosystem, i.e. the Water, Earth, Air and Fire Micro/Nanotechnologies Ecosystem, is conceived, leveraging the analogy with the four classical elements in nature, and is explained in details in the following pages, along with the discussion of some reference examples. In a nutshell, Earth and Air are the classical concepts of HW and Software (SW), respectively. Water is the novel formulation of the concept of HW, which, like water, is liquid in terms of functional characteristics and gains, at the same time, some features typical of the SW (i.e. Air). Fire, eventually, is the HW devoted to harvest, store and transfer energy, raising its level of abstraction to the concept of heat, which flows from warmer to cooler bodies.

The WEAF Mnecosystem: a perspective of MEMS/NEMS technologies as pillars of future 6G, tactile internet and super-IoT

Self-tuning system for multistandard active RC filters

The paper studies the peculiarities of processing big data in industrial systems. Existing methods of optimizing user information and the effectiveness of recommender systems have been considered. The Funk SVD algorithm was analyzed, and the practicality of its application to large sparse data was determined. A modification of the Funk SVD algorithm was proposed, which employs a smaller amount of user data to form recommendations. It was also suggested to use additional parameters that better characterize the services for more accurate work of recommender systems. Simulations were carried out, and a decrease in the duration and an increase in the accuracy of calculations using the proposed methods were determined. 

Model of Large Sparse Datasets Processing Efficiency in IIOT

IoT data is collected in real time and is treated as highly reliable data because of its high precision. However, it often exhibits incomplete values for reasons such as sensor aging and failure, poor operating environment, and communication problems. The characteristics of IoT data transmitted with high precision and time series are suitable to use LSTM, which is one kind of RNN. In this paper, when applying LSTM to data quality improvement in IoT environment where data are collected simultaneously from several sensors, it is suggested that it is effective to construct LSTM individually for each sensor accuracy.

/pdf/improving-data-quality-using-a-deep-learning-network-3x77jze3n2.pdf

Improving data quality using a deep learning network

Existing network infrastructure is very heterogeneous in terms of connectivity options, service types, and user demands. The emergence of new technologies such as 5G mobile networks and Internet of Things introduces the additional degrees of complexity and heterogeneity. It results in more diverse requirements to the separate parts of information and communications systems such as fixed and wireless access networks. In this paper, we propose a new virtualized convergent platform based on software defined network architecture. Proposed platform uses elastic optical network infrastructure with flexible configuration for switching, multiplexing, data Hows distribution and quality of service maintenance. All these parameters are defined by software and dynamically managed by the controller, providing advantaged in terms of the infrastructure utilization efficiency, flexibility, scalability and reliability, and quality of service.

Network functions virtualization for flexible deployment of converged optical-wireless access infrastructure

The problem of effective Big Data processing in the Industrial Internet of Things was investigated in the work. The method of Federated Machine Learning as a tool for the analysis of large volumes of information in IIoT was described. The algorithm of Singular Value Decomposition was analyzed, which effectively analyzes the user data and the possibility of its modification. The increase of computational reliability using the FedSVD algorithm was investigated. The software modeling which confirms the efficiency of the proposed method has been carried out.

Big Data Analysis in IIoT Systems Using the Federated Machine Learning Method

This article is concerned with the review of the most important modern wireless data transfer standards, while at the same time researching the prospects of implementation of these modern communication standards domestically, in Ukraine, as well as internationally.

Prospects of implementation of modern cellular wireless data transfer standards

Rapid technological development and continuous data growth tends to increase the pressure on the existing network infrastructure. In order to tackle this challenge optical transport networks are continuously improved to enhance their capabilities. In this paper, we propose a novel deep learning- based data flows optimization in optical label switched networks. The key idea of the proposed approach is to use data acquisition form the network nodes in order to collect statistical information of the network performance. Statistical information is than used to train deep neural network and determine optimal nodes configuration to improve the overall network performance. Simulations results show that proposed data flows aggregation algorithm allows to improve bandwidth utilization, while providing acceptable latency and packet loss rate.

Deep Learning based Traffic Optimization in Optical Transport Networks

The modeling of linear antenna arrays, which ensure scanning of the direction of the main lobe in one of the planes, has been conducted. It has been shown that when changing the direction of their radiation, the value of the coefficient directional antenna action substantially changes. The causes of the specified disadvantage occurrence have been reviewed and the ways of eliminating, which are protected by the proposed patents, have been depicted. In a scanning antenna on the basis of antenna arrays for any direction of the main petal are used N radiators. For a normalized, relative to the wavelength, distance between them must be satisfied the condition dn $\lt 0.5$. This provides one main petal diagram for all directions of scanning: from the axial - directed along the antenna array axis; to the transverse - directed normal to its axis. But when the direction of the main petal from the axial to the transverse is changed, the directional action of the antenna is reduced by almost twice, which causes the deterioration of the indicators ot the radio communication system. The essence of increasing the efficiency of scanning antennas is to eliminate this disadvantage and is based on the proposed patents. Antenna array, with a pair N, is supplemented by additional N / 2 analogue emitters. The normalized distance between them increases to 2dn. The main emitters are used at axial radiation. But for transverse - only N / 2 main emitters with odd numbers are used and all N / 2 additional. The normalized distance between the selected emitters is 2dn. It is shown that for transverse radiation it is possible to increase the normalized distance by two times, and the directional action coefficient is almost the same as in the case of axial radiation.

Simulation and Efficiency Increasing of the Scanning Antennas Based on Linear Arrays

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