2021. 1st Issue

Volume XIII, Number 1 

Table of contents 

Full issue  (11,6 MB)



Pal Varga
Young researchers on radio communication advances, various machine learning applications, and traffic congestion in smart cities 
This issue of the Infocommunications Journal raises our spirits by presenting the papers of young researchers as first authors. The first six papers are selected from the distinguished topics and awarded works of the Scientific Student Association of the Faculty of Electrical Engineering and Informatics, BME. The two papers that close the issue are submitted to our open call by young researchers, as well. 




Árpád László Makara and László Csurgai-Horváth
Improved Model for Indoor Propagation Loss in the 5G FR2 Frequency Band 
One of the latest developments today is the 5G, or 5th generation mobile network. In addition to a number of innovations, the new system also includes millimeter-wavelength frequency ranges denoted with FR2, that formerly not applied for these specific purposes. Proper management of the transmitter and receiver antenna beams is required for efficient communication in this frequency range. For future use, the simplest implementation way is electronically shaping the antenna beams by an algorithm to orient the antennas in the best possible direction. The prerequisites for these algorithms are appropriate propagation models, which are currently lacking, and those that publicly available are not accurate enough for practical use.

DOI: 10.36244/ICJ.2021.1.1


Ádám Marosits, Zsolt Tabi, Zsófia Kallus, Péter Vaderna, István Gódor, and Zoltán Zimborás
Exploring Embeddings for MIMO Channel Decoding on Quantum Annealers 
Quantum Annealing provides a heuristic method leveraging quantum mechanics for solving Quadratic Unconstrained Binary Optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for a wide range of use cases. In particular, a novel device, the D-Wave Advantage has been recently released. In this paper, we study the applicability of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in centralized RAN. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions can only use approximate methodologies, Kim et al. [1] leveraged Quantum Annealing for large MIMO problems with phase shift keying and quadrature amplitude modulation scenarios. Here, we extend upon their work and present embedding limits for both more complex modulation and higher receiver / transmitter numbers using the Pegasus P16 topology of the D-Wave Advantage system.

DOI: 10.36244/ICJ.2021.1.2


Donát Takács, Boldizsár Markotics, and Levente Dudás
Processing and Visualizing the Low Earth Orbit Radio Frequency Spectrum Measurement Results From the SMOG Satellite Project 
December 6, 2019, the second and third Hungarian satellites, SMOG-P and ATL-1 (both having been developed at the Budapest University of Technology and Economics) were launched. They both had a radio frequency spectrum analyzer on board, which was used to measure for the first time the strength of radio frequency signals radiated into space by terrestrial digital TV transmitters – that can be detected in orbit around the Earth. In this paper, we present how two- and three-dimensional radiosmog maps were created from raw data received from space. The goal of this paper is to demonstrate the process of creating these maps from the raw data collected; the analysis of the results visible in these maps is beyond the scope of the present discussion. 

DOI: 10.36244/ICJ.2021.1.3


Gábor Szűcs and Marcell Németh
Double-View Matching Network for Few-Shot Learning to Classify Covid-19 in X-ray images 
The research topic presented in this paper belongs to small training data problem in machine learning (especially in deep learning), it intends to help the work of those working in medicine by analyzing pathological X-ray recordings, using only very few images. This scenario is a particularly hot issue nowadays: how could a new disease for which only limited data are available be diagnosed using features of previous diseases? In this problem, so-called few-shot learning, the difficulty of the classification task is to learn the unique feature characteristics associated with the classes. Although there are solutions, but if the images come from different views, they will not handle these views well. We proposed an improved method, so-called Double-View Matching Network (DVMN based on the deep neural network), which solves the few-shot learning problem as well as the different views of the pathological recordings in the images. The main contribution of this is the convolutional neural network for feature extraction and handling the multi-view in image representation. Our method was tested in the classification of images showing unknown COVID-19 symptoms in an environment designed for learning a few samples, with prior meta-learning on images of other diseases only. The results show that DVMN reaches better accuracy on multi-view dataset than simple Matching Network without multi-view handling.

DOI: 10.36244/ICJ.2021.1.4


Daniel Vajda, Adrian Pekar, and Karoly Farkas
Towards Machine Learning-based Anomaly Detection on Time-Series Data 
The complexity of network infrastructures is exponentially growing. Real-time monitoring of these infrastructures is essential to secure their reliable operation. The concept of telemetry has been introduced in recent years to foster this process by streaming time-series data that contain feature-rich information concerning the state of network components. In this paper, we focus on a particular application of telemetry — anomaly detection on time-series data. We rigorously examined state-of-the-art anomaly detection methods. Upon close inspection of the methods, we observed that none of them suits our requirements as they typically face several limitations when applied on time-series data. This paper presents Alter-Re2, an improved version of ReRe, a state-of-the-art Long Short- Term Memory-based machine learning algorithm. Throughout a systematic examination, we demonstrate that by introducing the concepts of ageing and sliding window, the major limitations of ReRe can be overcome. We assessed the efficacy of Alter-Re2 using ten different datasets and achieved promising results. Alter-Re2 performs three times better on average when compared to ReRe.

DOI: 10.36244/ICJ.2021.1.5


Attila M. Nagy, and Vilmos Simon
Traffic congestion propagation identification method in smart cities 
Managing the frequent traffic congestion (traffic jams) of the road networks of large cities is a major challenge for municipal traffic management organizations. In order to manage these situations, it is crucial to understand the processes that lead to congestion and propagation, because the occurrence of a traffic jam does not merely paralyze one street or road, but could spill over onto the whole vicinity (even an entire neighborhood). Solutions can be found in professional literature, but they either oversimplify the problem, or fail to provide a scalable solution. In this article, we describe a new method that not only provides an accurate road network model, but is also a scalable solution for identifying the direction of traffic congestion propagation. Our method was subjected to a detailed performance analysis, which was based on real road network data. According to testing, our method outperforms the ones that have been used to date.

DOI: 10.36244/ICJ.2021.1.6



Hamid Garmani, Driss Ait Omar, Mohamed El Amrani, Mohamed Baslam, and Mostafa Jourhmane
Joint Beacon Power and Beacon Rate Control Based on Game Theoretic Approach in Vehicular Ad Hoc Networks 
In vehicular ad hoc networks (VANETs), each vehicle broadcasts its information periodically in its beacons to create awareness for surrounding vehicles aware of their presence. But, the wireless channel is congested by the increase beacons number, packet collision lost a lot of beacons. This paper tackles the problem of joint beaconing power and a beaconing rate in VANETs. A joint utilitybased beacon power and beacon rate game are formulated as a non-cooperative game and a cooperative game. A three distributed and iterative algorithm (Nash Seeking Algorithm, Best Response Algorithm, Cooperative Bargaining Algorithm) for computing the desired equilibrium is introduced, where the optimal values of each vehicle beaconing power and beaconing rate are
simultaneously updated at the same step. Extensive simulations show the convergence of a proposed algorithm to the equilibrium and give some insights on how the game parameters may vary the game outcome. It is demonstrated that the Cooperative Bargaining Algorithm is a fast algorithm that converges the equilibrium. 

DOI: 10.36244/ICJ.2021.1.7


Mohammad Moghadasi and Gabor Fazekas
Segmentation of MRI images to detect multiple sclerosis using non-parametric, non-uniform intensity normalization and support vector machine methods 
Multiple sclerosis (MS) is an inflammatory, chronic, persistent, and destructive disease of the central nervous system whose cause is not yet known but can most likely be the result of a series of unknown environmental factors reacting with sensitive genes. MRI is a method of neuroimaging studies that results in better image contrast in soft tissue. Due to the unknown cause of MS and the lack of definitive treatment, early diagnosis of this disease is important. MRI image segmentation is used to identify MS plaques. MRI images have an image error that is often called non-uniform light intensity. There are several ways to correct non-uniform images. One of these methods is Nonparametric Non-uniform intensity Normalization (N3). This method sharpens the histogram. The aim of this study is to reduce the effect of bias field on the MRI image using N3 algorithm and pixels of MRI images clustered by k-means algorithm. The dimensionality of the data is reduced by Principal Component Analysis (PCA) algorithm and then the segmentation is done by Support Vector Machine (SVM) algorithm. Results show that using the proposed system could diagnose multiple sclerosis with an average accuracy of 93.28%.

DOI: 10.36244/ICJ.2021.1.8



CNSM 2021 / 17th International Conference on Network and Service Management
CNSM 2021, Izmir, Turkey

GLOBECOM 2021 / IEEE Global Communications Conference
IEEE GLOBECOM, Madrid, Spain



Guidelines for our Authors





Technical Co-Sponsors





National Cooperation Fund, Hungary