No. 3 (2021)
Published:
2021-09-30
ARTICLES FROM THIS ISSUE
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Enabling Power Beacons and Wireless Power Transfers for Non-Orthogonal Multiple Access Networks
Abstract
This paper studies downlink cellular networks relying on non-orthogonal multiple access (NOMA). Specifically, the access point (AP) is able to harvest wireless power from the power beacon (PB). In the context of an AP facilitated with multiple antennas, the transmit antenna selection procedure is performed to process the downlink signal, with the transmission guaranteed by energy harvesting. Therefore, a wireless power transfer-based network is introduced to overcome power outages at the AP. In particular, an energy-constrained AP harvests energy from the radio frequency signals transmitted by the PB in order to assist in transmitting user data. Outage performance and ergodic capacity are evaluated with the use of closed-form expressions. In order to highlight some insights, approximate computations are provided. Finally, numerical simulations are performed to confirm the benefits of combining the downlink NOMA transmission and the transmit power scheme at the AP in order to serve a multitude of users
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Evaluation of Radio Channel Utility using Epsilon-Greedy Action Selection
Abstract
This paper presents an algorithm that supports the dynamic spectrum access process in cognitive radio networks by generating a sorted list of best radio channels or by identifying those frequency ranges that are not in use temporarily. The concept is based on the reinforcement learning technique named Q-learning. To evaluate the utility of individual radio channels, spectrum monitoring is performed. In the presented solution, the epsilon-greedy action selection method is used to indicate which channel should be monitored next. The article includes a description of the proposed algorithm, scenarios, metrics, and simulation results showing the correct operation of the approach relied upon to evaluate the utility of radio channels and the epsilon-greedy action selection method. Based on the performed tests, it is possible to determine algorithm parameters that should be used in this proposed deployment. The paper also presents a comparison of the results with two other action selection methods
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Developing RF Power Sensor Calibration Station in Direct Comparison Transfer System using Vector Network Analyzer
Abstract
Calibration of RF power sensors is crucial issue in RF power measurements. Many calibration laboratories use the direct comparison transfer system with a signal generator and a power splitter. Increasing performance of modern vector network analyzers makes it possible to perform a power sensor calibration with acceptable uncertainties. The main advantage when using a VNA is a simple measurement setup with a wide frequency range (up to 50 GHz, limited only by the VNA and the standard power sensor), where all of required components, i.e. signal generator, a directional coupler and a reference power indicator are built in the VNA technology. This paper reports performing a VNA-based RF power sensors calibration for 10 MHz – 18 GHz band, carried out in the Laboratory of Electric, Electronic and Optoelectronic Metrology at the National Institute of Telecommunications in Warsaw, Poland. In order to validate the proposed solution two of power sensors were calibrated at a reference laboratory. The validation consisted of two steps. At first, one of those characterized power sensors was calibrated at our laboratory in direct comparison transfer system. Finally, the results obtained from the VNA-based system were compared with the previously obtained ones
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Network Traffic Classification in an NFV Environment using Supervised ML Algorithms
Abstract
We have conducted research on the performance of six supervised machine learning (ML) algorithms used for network traffic classification in a virtual environment driven by network function virtualization (NFV). The performancerelated analysis focused on the precision of the classification process, but also in time-intensity (speed) of the supervised ML algorithms. We devised specific traffic taxonomy using commonly used categories, with particular emphasis placed on VoIP and encrypted VoIP protocols serve as a basis of the 5G architecture. NFV is considered to be one of the foundations of 5G development, as the traditional networking components are fully virtualized, in many cases relaying on mixed cloud solutions, both of the premise- and public cloud-based variety. Virtual machines are being replaced by containers and application functions while most of the network traffic is flowing in the east-west direction within the cloud. The analysis performed has shown that in such an environment, the Decision Tree algorithm is best suited, among the six algorithms considered, for performing classification-related tasks, and offers the required speed that will introduce minimal delays in network flows, which is crucial in 5G networks, where packet delay requirements are of great significance. It has proven to be reliable and offered excellent overall performance across multiple network packet classes within a virtualized NFV network architecture. While performing the classification procedure, we were working only with the statistical network flow features, leaving out packet payload, source, destination- and port-related information, thus making the analysis valid not only from the technical, but also from the regulatory point of view
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A Shared Cybersecurity Awareness Platform
Abstract
Ensuring a good level of cybersecurity of global IT systems requires that specific procedures and cooperation frameworks be adopted for reporting threats and for coordinating the activities undertaken by individual entities. Technical infrastructure enabling safe and reliable online collaboration between all teams responsible for security is an important element of the system as well. With the above taken into consideration, the paper presents a comprehensive distributed solution for continuous monitoring and detection of threats that may affect services that provision is essential to security and broadly understood the state’s economic interests. The said solution allows to collect, process and share distributed knowledge on hazard events. The partnership-based model of cooperation between the system’s users allows the teams to undertake specific activities at the central level, facilitates global cyber threat awareness, and enhances the process of predicting and assessing cyber risks in order to ensure a near-realtime response. The paper presents an overview of the system’s architecture, its main components, features, and threat intelligence tools supporting the safe sharing of information concerning specific events. It also offers a brief overview of the system’s deployment and its testing in an operational environment of NASK’s Computer Security Incident Response Team (CSIRT) and Security Operation Center (SOC) of essential services operators
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Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks
Abstract
In this paper, a new reinforcement learning intrusion detection system is developed for IoT networks incorporated with WSNs. A research is carried out and the proposed model RL-IDS plot is shown, where the detection rate is improved. The outcome shows a decrease in false alarm rates and is compared with the current methodologies. Computational analysis is performed, and then the results are compared with the current methodologies, i.e. distributed denial of service (DDoS) attack. The performance of the network is estimated based on security and other metrics
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Linear and Planar Array Pattern Nulling via Compressed Sensing
Abstract
An optimization method based on compressed sensing is proposed for uniformly excited linear or planar antenna arrays to perturb excitation of the minimum number of array elements in such a way that the required number of nulls is obtained. First, the spares theory is relied upon to formulate the problem and then the convex optimization approach is adopted to find the optimum solution. The optimization process is further developed by using iterative re-weighted l1- norm minimization, helping select the least number of the sparse elements and impose the required constraints on the array radiation pattern. Furthermore, the nulls generated are wide enough to cancel a whole specific sidelobe. Simulation results demonstrate the effectiveness of the proposed method and the required nulls are placed with a minimum number of perturbed elements. Thus, in practical implementations of the proposed method, a highly limited number of attenuators and phase shifters is required compared to other, conventional methods
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High Temperature Effects in Fused Silica Optical Fibers
Abstract
Fire-resistant fiber optic cables used in safety and monitoring systems playing an essential role in fire fighting and building evacuation procedures are required to temporarily maintain optical continuity when exposed to fire. However, the use of fused silica fiber at temperatures between 800◦C and 1000◦C is associated with two highly undesirable phenomena. Thermal radiation (incandescence) of optical fibers, with its intensity and spectral distribution being proportional to additional attenuation observed in the fiber’s hydroxyl absorption bands (“water peaks”) is one of them. The other consists in penetration of thermal radiation from the surroundings into the fiber, due to defects in glass, causing light scattering and resulting in fiber brittleness. Thermal radiation is a source of interference in fiber attenuation measurements performed during fire tests and affects normal operation of fiber optic data links in the event of a fire. In this article, results of laboratory tests performed on a telecom single mode and multimode fibers subjected to temperatures of up to 1000◦C are presented
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Speech-Based Vehicle Movement Control Solution
Abstract
The article describes a speech-based robotic prototype designed to aid the movement of elderly or handicapped individuals. Mel frequency cepstral coefficients (MFCC) are used for the extraction of speech features and a deep belief network (DBN) is trained for the recognition of commands. The prototype was tested in a real-world environment and achieved an accuracy rate of 87.4%
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Tractography Methods in Preoperative Neurosurgical Planning
Abstract
Knowledge of the location of nerve tracts during the surgical preoperative planning stage and during the surgery itself may help neurosurgeons limit the risk of causing neurological deficits affecting the patient’s essential abilities. Development of MRI techniques has helped profoundly with in vivo visualization of the brain’s anatomy, enabling to obtain images within minutes. Different methodologies are relied upon to identify anatomical or functional details and to determine the movement of water molecules, thus allowing to track nerve fibers. However, precise determination of their location continues to be a labor-intensive task that requires the participation of highly-trained medical experts. With the development of computational methods, machine learning and artificial intelligence, many approaches have been proposed to automate and streamline that process, consequently facilitating image-based diagnostics. This paper reviews these methods focusing on their potential use in neurosurgery for better planning and intraoperative navigation.
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COVID-19 Pandemic and Internet Traffic in Poland: Evidence from Selected Regional Networks
Abstract
The COVID-19 pandemic has forced governments all over the world to impose lockdowns keeping citizens at home in order to limit the virus spread rate. The paper compares weekly traffic samples captured in the selected nodes of the network managed by NASK – National Research Institute during the pre-lockdown period, i.e. between January 27 and February 3, 2020, with those captured between March 30 and April 6, 2020, i.e. after the lockdown was announced. The presented results show changes in network traffic observed during the periods of time in question and illustrate the evolution in the popularity of top network services
