No. 4 (2020)
Published:
2020-12-30
ARTICLES FROM THIS ISSUE
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Energy Efficiency in 5G Communications – Conventional to Machine Learning Approaches
Abstract
Demand for wireless and mobile data is increasing along with development of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (ER) applications. In order to handle ultra-high data exchange rates while offering low latency levels, fifth generation (5G) networks have been proposed. Energy efficiency is one of the key objectives of 5G networks. The notion is defined as the ratio of throughput and total power consumption, and is measured using the number of transmission bits per Joule. In this paper, we review state-of-the-art techniques ensuring good energy efficiency in 5G wireless networks. We cover the base-station on/off technique, simultaneous wireless information and power transfer, small cells, coexistence of long term evolution (LTE) and 5G, signal processing algorithms, and the latest machine learning techniques. Finally, a comparison of a few recent research papers focusing on energy-efficient hybrid beamforming designs in massive multiple-input multiple-output (MIMO) systems is presented. Results show that machine learningbased designs may replace best performing conventional techniques thanks to a reduced complexity machine learning encoder
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Time-shifted Pilot-based Scheduling with Adaptive Optimization for Pilot Contamination Reduction in Massive MIMO
Abstract
Massive multiple-input multiple-output (MIMO) is considered to be an emerging technique in wireless communication systems, as it offers the ability to boost channel capacity and spectral efficiency. However, a massive MIMO system requires huge base station (BS) antennas to handle users and suffers from inter-cell interference that leads to pilot contamination. To cope with this, time-shifted pilots are devised for avoiding interference between cells, by rearranging the order of transmitting pilots in different cells. In this paper, an adaptive-elephant-based spider monkey optimization (adaptive ESMO) mechanism is employed for time-shifted optimal pilot scheduling in a massive MIMO system. Here, user grouping is performed with the sparse fuzzy c-means (Sparse FCM) algorithm, grouping users based on such parameters as large-scale fading factor, SINR, and user distance. Here, the user grouping approach prevents inappropriate grouping of users, thus enabling effective grouping, even under the worst conditions in which the channel operates. Finally, optimal time-shifted scheduling of the pilot is performed using the proposed adaptive ESMO concept designed by incorporating adaptive tuning parameters. The efficiency of the adaptive ESMO approach is evaluated and reveals superior performance with the highest achievable uplink rate of 43.084 bps/Hz, the highest SINR of 132.9 dB, and maximum throughput of 2.633 Mbps
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MIMO Antenna Design and Optimization with Enhanced Bandwidth for Wireless Applications
Abstract
This paper demonstrates a compact MIMO (Multi Input Multi Output) fractal type antenna for ultra-wide band applications. The proposed antenna is manufactured on a lowcost substrate material and the design is analyzed for various iterations in terms of reflection coefficient, gain, and bandwidth. The 50 Ω transmission line feed is used for both fractal patches and a metamaterial structure is used as the ground plane. The proposed design achieved a wide-band frequency response between 5.8 and 15 GHz, with the reflection coefficient of less than –10 dB. Reduced mutual coupling, positive gain and stable radiation patterns were observed throughout the operating band as well. The bandwidth of 9.2 GHz is achieved with the use of a metamaterial structure on the ground plane. The ECC and diversity gain obtained prove the excellent diversity performance of the antenna. The design was simulated using HFSS software and was tested in a lab
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A Comprehensive Survey on Resource Management in Internet of Things
Abstract
Efficient resource management is a challenging task in distributed systems, such as the Internet of Things, fog, edge, and cloud computing. In this work, we present a broad overview of the Internet of Things ecosystem and of the challenges related to managing its resources. We also investigate the need for efficient resource management and the guidelines given/suggested by Standard Development Organizations. Additionally, this paper contains a comprehensive survey of the individual phases of resource management processes, focusing on resource modeling, resource discovery, resource estimation, and resource allocation approaches based on performance parameters or metrics, as well as on architecture types. This paper presents also the architecture of a generic resource management enabler. Furthermore, we present open issues concerning resource management, pointing out the directions of future research related to the Internet of Things
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Designing a Compact Microstrip Antenna Using the Machine Learning Approach
Abstract
This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results
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Transmission of Disaster Warnings via Control Channels in Cellular Networks
Abstract
According to United Nations reports, natural disasters caused, worldwide, approximately 100,000 deaths and affected 175 million people each year between 2004 and 2013. To reduce those numbers, countries around the globe have made specific arrangements enabling them to warn the population about imminent disasters, in order to evacuate the area in due time. But providing such warnings in areas where no Internet access is available poses a great challenge. In this paper, we proposed a method to transmit early warning messages via UMTS cellular networks, while relying on spare extensions of control channels (FACH). The results obtained are validate based on their comparison with theoretical considerations and are also benchmarked against the 3GPP standard. The results show that messages may be sent faster than with the use of the 3GPP standard
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Detection of DDoS Attacks in OpenStack-based Private Cloud Using Apache Spark
Abstract
Security is a critical concern for cloud service providers. Distributed denial of service (DDoS) attacks are the most frequent of all cloud security threats, and the consequences of damage caused by DDoS are very serious. Thus, the design of an efficient DDoS detection system plays an important role in monitoring suspicious activity in the cloud. Real-time detection mechanisms operating in cloud environments and relying on machine learning algorithms and distributed processing are an important research issue. In this work, we propose a real-time detection of DDoS attacks using machine learning classifiers on a distributed processing platform. We evaluate the DDoS detection mechanism in an OpenStack-based cloud testbed using the Apache Spark framework. We compare the classification performance using benchmark and real-time cloud datasets. Results of the experiments reveal that the random forest method offers better classifier accuracy. Furthermore, we demonstrate the effectiveness of the proposed distributed approach in terms of training and detection time
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A New Parameterized Model for Determining Quality of Online Video Service Using Modern H.265/HEVC and VP9 Codecs
Abstract
This paper describes a new measurement method (VS model) for determining the quality of online video services relying on modern H.265/HEVC and VP9 codecs. The said method has been developed on the basis of VQuad-HD curves (according to ITU-T J.341). This model does not refer to signal analysis, but protocol analysis instead. The parameters used are: type of video codec, encoding rate, transport technique, packet loss and burst size. The method may be implemented quickly and easily, which is one of the great advantages when using this method to measure QoS
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An Online Stream Monitoring Algorithm for Fraud Detection in the Transport of Goods
Abstract
The process of monitoring vehicles used in road transports plays an important role in detecting fraud committed by drivers. Algorithm designers face a number of challenges, including large number of vehicles monitored, demands related to online calculations, and ability to easily explain fraud alarms triggered to supervisors who make final decisions about actions to be taken. In this paper, we propose rather general, lightweight stream, online heuristics. The vehicle’s position is periodically controlled by a GNSS device. The algorithm detects potential illegal activities along the route between the origin and the destination. Anomalies in the vehicle’s trajectory are detected, based on a multi-resolution analysis of the economy of routes. The economy metric is easily understood and verifiable by controllers. The solution is also capable of identifying clearly suspicious trajectories that popular geofencing approaches would overlook. The scale on which the solution may be adopted is obtained thanks to the stream – like nature of the algorithm: essentially, the resources used do not increase along with the size of the input stream (the number of GNSS frames generated for the vehicle). An experiment illustrating the algorithm’s viability is presented as well
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A Novel System Architecture for an Improved Self-care Solution – Conceptual Design and Key Components
Abstract
The high penetration rate that mobile devices enjoy in to day’s society has facilitated the creation of new digital services, with those offered by operators and content providers standing out. However, even this has failed to encourage consumers to express positive opinions on telecommunication services, especially when compared with other sectors. One of the main reasons of the mistrust shown is the low level of quality of customer service provided an area that generates high costs for the operators themselves, due to the high number of people employed at call centers in order to handle the volume of calls received. To face these challenges, operators launched self-care applications in order to provide customers with a tool that would allow them to autonomously manage the services they have subscribed. In this paper, we present an architecture that provides customized information to customers – a solution that is separate from mobile operating systems and communication technologies
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Autonomous Instrumentation for Measuring Spontaneous Electromagnetic Emissions in Mining
Abstract
This article presents the design of an innovative receiver capable of identifying electric and magnetic components of electromagnetic fields. The receiver senses and records electromagnetic disturbances generated as mine tunnels collapse. It offers excellent operating specification and the ability to sense and log magnetic and electrical component strength values in real time. The paper analyzes the data obtained with the use of a system installed in a working mine and attempts to determine hazards resulting from increased rock stress levels that, cause spontaneous EM emissions
