https://jcis.sbrt.org.br/jcis/issue/feed Journal of Communication and Information Systems 2022-07-26T15:37:46-03:00 Rausley A. A. de Souza and José Cândido S. Santos Filho jcis.editor@sbrt.org.br Open Journal Systems <p>The Journal of Communication and Information Systems (JCIS) features high-quality, peer-reviewed technical papers in several areas of communications and information systems. The JCIS is jointly sponsored by the Brazilian Telecommunications Society (SBrT) and the IEEE Communications Society (ComSoc). As from June 2020, Prof. Rausley Adriano Amaral de Souza from National Institute of Telecommunications (Inatel) and Prof. José Cândido Silveira Santos Filho from University of Campinas (UNICAMP) are the Editors-in-Chief of the JCIS.&nbsp;</p> <p>There are no article publication or submission charges. Previous editions of the JCIS can be accessed <a href="/index.php/JCIS/issue/archive" target="_blank" rel="noopener">here</a>.</p> <p>This is an open access journal which means that all content is freely available without charge to the user or his/her institution, permanently accessible online immediately upon assignment of the DOI. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.</p> <p>ISSN: 1980-6604</p> https://jcis.sbrt.org.br/jcis/article/view/755 On Symmetric Channels and Codes Over the Quaternion Group 2022-07-26T15:37:46-03:00 Jorge P Arpasi arpasi@gmail.com <p>In this paper we study symmetric channels and group codes over the quaternion group Q<sub>8</sub>.&nbsp;We show that, related to these channels, there is the number C<sub>Q8</sub><sub>,</sub> called group-capacity,&nbsp;which is less or equal than the capacity of the channel. Also we show that C<sub>Q8&nbsp;</sub>is an upper bound for the rate of any reliable quaternion group code. Finally we show&nbsp;that the group-capacity equals the channel capacity.</p> 2022-07-26T15:37:46-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/823 Performances of 2r16APSK and DVB-S2 16APSK Modulations over a Two-Link Satellite Channel 2022-07-20T13:49:17-03:00 Dayan Adionel Guimarães dayan@inatel.br <p>In this paper, the 2r16APSK modulation is contrasted&nbsp;with the 16APSK modulation adopted in the digital&nbsp;video broadcast standard, the DVB-S2, aiming at verifying if&nbsp;the 2r16APSK can be considered an alternative choice for the&nbsp;DVB-S2 and other alike communication systems when subjected&nbsp;to nonlinear distortions. To this end, the performances of both&nbsp;modulations are assessed in terms of the metrics: bit error&nbsp;rate (BER), constellation figure of merit (CFM), peak-to-average&nbsp;power ratio (PAPR), total degradation (TD) versus input back-off&nbsp;(IBO), and spectral regrowth, when the transmitted signal goes&nbsp;through a two-link satellite channel under memoryless nonlinear&nbsp;distortion produced by a traveling wave tube amplifier (TWTA),&nbsp;which is described by the Saleh model. The results show that the&nbsp;2r16APSK modulation is indeed an alternative choice.</p> 2022-07-20T13:49:17-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/817 Resource Allocation for Maximizing Spectral Efficiency in a Multiuser MFSK System 2022-07-06T10:03:36-03:00 Manish Sharma manish@ita.br Daniel Basso Ferreira danielbf@ita.br <p>In this paper we explore strategies to improve spectral efficiency values of a multiuser M-ary Frequency Shift Keying (MFSK) system when a fast and frequency selective fading channel with limited bandwidth is available and $L$ receiving antennas are present at the base station. Two options are considered: bandwidth splitting and cell splitting, where random user distribution and the effects of antenna directivity on signal to noise ratio was considered. Results show that in this situation the best case scenario is the one in which all users share the whole bandwidth instead of splitting it. Also, given the limitation on available antennas and radio frequency receiver chains at the base station, it is better to split cell antennas into as many sectors as possible, even at the cost of losing spatial diversity in each sector. Given the constraints, sectoring allows the system to reach values of spectral and energy efficiencies unachievable by spatial diversity by itself.</p> 2022-07-06T10:03:36-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/828 On the Uniqueness of the Quasi-Moment-Method Solution to the Pathloss Model Calibration Problem 2022-07-01T11:29:35-03:00 Hisham Abubakar Muhammed, Mr. hmuhammad@unilag.edu.ng Ayotunde Abimbola Ayorinde, Dr. aayorinde@unilag.edu.ng Francis Olutunji Okewole, Engr. fokewole@unilag.edu.ng Michael Adedosu Adelabu, Dr. madelabu@unilag.edu.ng Ike Mowete amowete@unilag.edu.ng <p><strong>Investigations in this paper focus on establishing the uniqueness properties of the Quasi-Moment-Method (QMM) solution to the problem of calibrating nominal radiowave propagation pathloss prediction models. Nominal (basic) prediction models utilized for the investigations, were first subjected to QMM calibrations with measurements from three different propagation scenarios. Then, the nominal models were recast in forms suitable for Singular Value Decomposition (SVD) calibration before being calibrated with both the SVD and QMM algorithms. The prediction performances of the calibrated models as evaluated in terms of Root Mean Square Prediction Error (RMSE), Mean Prediction Error (MPE), and Grey Relational Grade-Mean Absolute Percentage Error (GRG-MAPE) very clearly indicate that the uniqueness of&nbsp; QMM-calibrations of basic pathloss models is more readily observable, when the basic models are recast in forms specific to SVD calibration. In the representative case of calibration with indoor-to-outdoor measurements, RMSE values were recorded for QMM-calibrated nominal models as 5.2639dB for the ECC33 model, and 5.3218dB for the other nominal models. Corresponding metrics for the alternative (rearranged) nominal models emerged as 5.2663dB for the ECC33 model and 5.2591dB for the other models. A similar general trend featured in the GRG-MAPE metrics, which for both SVD and QMM calibrations of all the alternative models, was recorded as 0.9131, but differed slightly (between 0.9138 and 0.9196) for the QMM calibration of the nominal models. The slight differences between these metrics (due to computational round-off approximations) confirm that when the &nbsp;components of basic models are linearly independent, the QMM solution is unique. Planning for wireless communications network deployment may consequently select any basic model of choice for QMM-calibration, and hence, identify relative contributions to pathloss by the model’s component parts.</strong></p> 2022-07-01T00:00:00-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/820 User-Level Handover Decision Making Based on Machine Learning Approaches 2022-06-22T13:07:51-03:00 João Lima jsales@cpqd.com.br Alvaro Medeiros alvaro@engenharia.ufjf.br Eduardo Aguiar eduardo.aguiar@engenharia.ufjf.br VICENTE ANGELO DE SOUSA JUNIOR vicente.sousa@ufrn.br Tarciana Guerra tarciana.guerra.051@ufrn.edu.br <p>This letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulator <em>ns-3</em>. The comparison encompasses classical machine learning approaches, such as KNN, SVM, and neural networks, but also state-of-the-art fuzzy logic systems and latter boosting machines. The results indicate that SVM and MLP are the most suitable for the classification of the best handover target, although fuzzy system SOFL can perform similarly with lower processing time. Additionally, for the download time estimation, LightGBM provides the smallest error with short processing time, even in hard propagation scenarios.</p> 2022-06-22T13:03:00-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/821 Performance Assessment of DTN and VANET Protocols for Transmitting Periodic Warning Messages in High Vehicular Density Networks 2022-06-20T08:10:22-03:00 Alvaro Torres Amaya amaya@alunos.utfpr.edu.br Mauro Sergio Fonseca maurofonseca@utfpr.edu.br Alexandre Pohl pohl@utfpr.edu.br Ricardo Lüders luders@utfpr.edu.br <p>In recent years, routing protocols for Delay Tolerant Networks (DTN) have become appealing for vehicular ad-hoc networks (VANET), particularly for communication between vehicles in highly sparse environments. In such scenarios, network disconnections are frequent, and the establishment of stable source-destination links is scarce. This work addresses the performance of four DTN and two traditional VANET protocols when the vehicular density becomes high in a short-scale scenario. In this case, vehicles may need to communicate with near-located neighbors, and traffic conditions can rapidly change from low to high congested areas. Specifically, we evaluate how DTN and traditional VANET routing protocols deal with the transmission of warning messages that require message generation rates higher than usually found in the literature. The results show that the traditional VANET protocols outperform the DTN approaches considered in this work for transmitting warning messages in high vehicular-density scenarios. The results also shed light on features that DTN protocols should consider to improve the performance in such scenarios.</p> 2022-06-20T08:10:21-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/793 A New Approach for FSS Design in 3.5GHz Based on General Neural Network Model by using Multi-objective Sailfish Optimization Algorithm 2022-05-29T16:48:50-03:00 Nelson Mateus Santos nelsonmateusbass@gmail.com Miercio Neto miercio@ufpa.br Jasmine Araujo jasmine.araujo@gmail.com Fabricio Barros fjbbrito@gmail.com Gervasio Cavalcante gervasio@ufpa.br Edemir Matos edemir.matos@gmail.com Rafael Vieira rafaelfogarolli@outlook.com <p><span class="fontstyle0">This work approaches a bioinspired hybrid multiobjective optimization technique associated with a general regression neural network as a proposal to synthesize the geometry and the dimensions of a frequency selective surface (FSS) for electromagnetic wave filtering in 5G applications. This new hybrid technique associates the bio-inspired algorithm known as the Sailfish Optimizer (SFO), together with a GRNN net to obtain the parameters for constructing the filter.In this study, the focus is on the application of the technique as a tool for the design and the synthesis of FSS, which has the shape of a square spiral unitary cell, printed on a fiberglass substrate plate (FR4). The objectives of the optimization process are to set the resonant frequency of the FSS to 3.5 GHz and the&nbsp; perating bandwidth to 0.8 GHz. It is reported a good agreement between&nbsp;the simulated and measured results.</span></p> 2022-05-29T16:48:50-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/798 New Reversible Data Hiding Algorithm Based on Edge Detection and PVO Mechanisms 2022-04-18T08:06:11-03:00 Thai-Son Nguyen thaison@tvu.edu.vn Hoang-Nam Tram tramhoangnam@tvu.edu.vn <p>Reversible data hiding technique is able to restore the cover data exactly after data extraction. This paper presents a new reversible data hiding scheme based on the combination of edge detection and pixel value ordering mechanism is proposed. In the proposed scheme, the cover image is divided into smooth or rough areas by Canny edge detection algorithm. Then, the secret data is embedded into pixels in the smooth areas based on pixel value ordering algorithm to obtain better image quality of marked images. The experimental results demonstrate that our scheme achieves better visual quality than those of state-of-the-art schemes while guaranteeing the reversibility.</p> 2022-04-18T08:06:11-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/805 Understanding Ransomware Actions Through Behavioral Feature Analysis 2022-04-11T14:42:44-03:00 Caio Carvalho Moreira caiomoreira@ufpa.br Claudomiro de Souza de Sales, Jr. cssj@ufpa.br Davi Carvalho Moreira davi.moreira@eletronorte.gov.br <p>Crypto ransomware attacks have substantially increased in recent years, and owing to their highly profitable&nbsp; nature, this growth will evidently escalate in the future. To better understand this malware and help developers of ransomware detection systems build more robust and reliable solutions, this study investigates ransomware actions during the destruction phase through behavioral feature analysis. We used a dataset with 1524 samples and 30 967 features representing the actions conducted using 582 types of ransomware and 942 good applications (goodware). Six representative and widely used classification algorithms were applied as auxiliary tools to investigate the behavior of these attacks: Naive Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM). We achieved an accuracy of 98.48%, balanced accuracy of 98.35%, precision of 98.17%, recall of 97.82%, F-measure of 97.98%, and ROC AUC of 99.87% by using RF for 462 features of the resultant dataset. We propose a new criterion to determine the feature group relevance and a method to distinguish the features that are most related to ransomware and goodware. Our main conclusions are as follows: Application Programming Interface (API) calls are the most relevant feature group, achieving alone a balanced accuracy of 96.49%; native encryption Windows APIs are not crucial for ransomware classification; and the most significant features of ransomware tend to involve handling the thread/process, physical memory operation, and communication, whereas goodware features are more likely to indicate virtual memory, files, directories, and resource operations.</p> 2022-03-10T19:01:18-03:00 ##submission.copyrightStatement## https://jcis.sbrt.org.br/jcis/article/view/759 Digital TV Channel Prediction using Clustering Algorithms and Statistical Learning 2022-03-08T11:50:40-03:00 Daniel da Costa Vidal danielvidal@usp.br Tadeu Ferreira tadeu_ferreira@id.uff.br Pedro Castellanos pcastellanos@id.uff.br <p>Due to the rise of new communication services, more portions of the electromagnetic spectrum must be relocated and their distribution optimized. With the digitization of the open TV service, it was observed that the distribution of channels in&nbsp; he frequency band destined for this service generated an inefficient use of the radio spectrum. These unused frequency bands are the so-called void spaces. To establish efficient spectrum use, it is important to identify these spectrum gap&nbsp; opportunities and use according to certain criteria. In this article, machine learning algorithms are proposed to identify new spectrum opportunities, through the signal levels received in the UHF frequency range of the Digital TV system. These&nbsp; spectrum opportunities are generated from natural or artificial obstacles present in the propagation environment. Two measurement campaigns were carried out in a suburban area to obtain the level of the signal received in an area of approximately 240,000 square meters. From the received power values, machine learning algorithms were used to make prediction of the received signal levels. By using a reception threshold, it is possible to identify the shadow regions and possible&nbsp; spectrum opportunities.</p> 2022-03-08T11:50:40-03:00 ##submission.copyrightStatement##