No. 2 (2025)
Explore the current issue of the JTIT
The current issue of the Journal of Telecommunication and Information Technology (JTIT) offers high-quality original articles and showcases the results of key research projects conducted by recognized scientists and dealing with a variety of topics involving telecommunications and information technology, with a particular emphasis placed on the current literature, theory, research and practice.
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Published:
2025-06-30
Full Issue
ARTICLES FROM THIS ISSUE
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Using a Half-mode SIW Loaded with Slots to Realize a Compact UWB Bandpass Filter
Abstract
This paper presents an ultra-wideband bandpass filter (UWB-BPF) based on a half-mode substrate-integrated waveguide (HMSIW) structure with a passband that spans from 4.12 to 11 GHz and has a center frequency of 7.56 GHz with a fractional bandwidth of 91%. Less than 0.3 dB of insertion loss was achieved through the passband, with the return loss being better than 19 dB. Two transmission zeros were generated, contributing to the 5.24 GHz out-of-band rejection. The proposed filter is compact, making it a good choice for space-constrained systems. In addition, a notch response has been utilized to comply with standards issued by applicable regulatory bodies, by blocking some bands located in the passband. Ansys EDT software was used to implement the design and determine the response of the proposed structure, while Keysight ADS was used to validate the results.
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An Artificial Intelligence-based Handover Triggering and Management Mechanism for 5G Ultra-dense Networks to Improve Handover Authentication
Abstract
The emergence of 5G ultra-dense networks has gained considerable attention, as solutions of this kind enable rapid and intelligent device connectivity, thus ushering in a new era of high-speed communications. Nevertheless, the process of managing mobility across varying inter-frequency strategies increases interference and complexity. The development of a reliable handover algorithm is crucial for high-quality service, especially in ultra-dense networks with small cells. However, frequent handovers, ping-pong effects, and load-balancing issues arise due to the random and dense deployment of small cells. Additionally, ensuring secure and smooth handover authentication is critical, due to an increased risk of frequent transitions of users across different networks. In such a context, this research focuses on triggering handovers and managing 5G mobile networks, all while protecting sensitive data. We introduce an artificial intelligence-based approach aimed at improving handover initiation and management processes, leveraging Boruta random forest optimization (BRFO) to fine-tune handover margins and identify optimal trigger points for handovers. In addition, an impulsive graph neural network (IGNN) is utilized as a decision framework to predict and minimize unnecessary handovers, thus improving stability in small cell environments. Simulation results demonstrate that the proposed methodology significantly enhances handover management, strengthens authentication, and effectively mitigates a variety of attacks in 5G ultra-dense networks.
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Synthesizing Wide-beam Array Patterns Using Phase-only Control and Trapezoidal Amplitudes for Satellite-based Internet Access
Abstract
Low Earth orbit satellite systems are capable of providing global Internet access due to their high downlink rate and low link budget. In such systems, wide beam array patterns are used to efficiently cover the required areas. In this paper, two efficient methods based on phase-only element excitation control for designing antenna arrays with required broaden beams are introduced. The first method, which is a simple algebraic approach, uses quadratic phase excitation while the amplitudes are chosen to be trapezoid. In the second method, an optimization algorithm is used to optimize the phase excitations of the array elements, while the amplitudes are still kept as a trapezoidal taper. Moreover, the use of trapezoidal-based amplitude excitations in both presented methods provides many desirable features compared to other conventional tapers. This is mainly due to the unique geometrical shape of the trapezoid taper, where the central coefficients have magnitudes of ones and the sided coefficients have decayed magnitudes. Simulations are presented to validate the proposed methods in which the beam width and maximum level of the radiated field were compared with those obtainable from the conventional standard Woodward-Lawson array.
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Analysis of Pyramidal Microwave Absorbers for Enhanced Performance in 1-10 GHz Frequency Range
Abstract
One of the main applications of microwave absorbers is in anechoic chambers, where the walls are lined with pyramidal foam impregnated with a lossy material. This paper investigates the impact that various design parameters of pyramidal microwave absorbers exert on their performance, with the aim of finding the best design values that ensure better operational properties. Typical pyramid absorbers were investigated by conducting simulations with the use of the CST Microwave Suite simulator, across the frequency range of 1-10 GHz, at various angles of the incident wave. The investigations also considered absorbers backed by conducting plates that are used in shielded anechoic chambers. The study shows that higher permittivity leads to higher reflection, while increased loss tangent improves absorption, and the same applies to magnetic materials. Larger pyramid heights lead to lower reflection, but only in the case of thicker absorbers. A pyramidal absorber with the height of 16 cm, designed using lossy material with permittivity and permeability of 1.5 and loss tangent of 0.5 achieved a reflection coefficient that was lower than -60 dB for frequencies between 3 and 10 GHz. The results are useful in designing absorbers relying on materials that offer only dielectric or magnetic properties, or that combine both of them to achieve enhanced performance.
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UAV-BS-based Hybrid OMA-NOMA System with Multiple Antennas for Multi-user Communication
Abstract
In this paper, an unmanned aerial vehicle (UAV) using hybrid orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) solutions aided by multiple input multiple output (MIMO) technology is proposed to provide wireless communication for ground users (GUs). The proposed OMA-NOMA-MIMO system aims to improve throughput and spectrum efficiency. Additionally, it also strives to maximize the sum rate while achieving good user fairness. UAVs are considered as base stations (BSs) that provide services to users in multiple real-life scenarios, e.g. during natural disasters. They also enable aerial surveillance and help establish BSs during mass events. A pairing algorithm is proposed for far-near NOMA users with an optimized power allocation (PA) mechanism to improve the performance of NOMA-UAV-BS. Channels between UAV-BS and GU are established as being of the line-of-sight (LoS) and non-line-of-sight (NLoS) varieties, taking into consideration the angle of departure (AoD) and the angle of arrival (AoA). The results obtained demonstrate that NOMA performs best in specific scenarios, while OMA overcomes NOMA in others. The outcomes of the project may be utilized to control the transmission performed by the UAV-BS by serving the GUs depending on the required quality of service.
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Using Modified Gorti-enhanced Homomorphic Cryptosystem to Improve Security of ECG Signal
Abstract
While offering vast data storage capabilities, cloud computing poses numerous security- and privacy-related challenges. This requires robust security measures, particularly for sensitive data, such as electrocardiograms (ECG). Homomorphic encryption (HE) emerges as a promising solution by enabling secure computations to be performed directly on encrypted data. This study introduces a novel approach to enhance the security of ECG data. We modified the Gorti-enhanced homomorphic cryptosystem (MEHC) method by optimizing its key generation procedure and then applied the linear congruential generator (LCG) algorithm to create a list of huge prime integers. Furthermore, we increased the modulus value and enlarged the message space. These enhancements boosted overall security by substantially improving immunity to factorization attacks. We used quantization and fixed-point representation to enhance the encryption process. As an additional security layer, an evaluation process has been added to the proposed algorithm which performs various mathematical operations homomorphically on the encrypted data, rather than on the original data. This modified algorithm enables efficient and secure encryption of ECG data while preserving the ability to reliably identify arrhythmias, such as bradycardia and tachycardia. Using the MIT-BIH arrhythmia database, the proposed MEHC system demonstrated high accuracy (98.48%), sensitivity (99.10%) and positive predictive value (99.33%), while effectively safeguarding the ECG data. These results validate the efficacy of the MEHC system and confirm its suitability for secure and reliable ECG signal processing in healthcare applications.
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ILP Optimized LSTM-based Autoscaling and Scheduling of Containers in Edge-cloud Environment
Abstract
Edge computing is a decentralized computing paradigm that brings computation and data storage closer to data sources, enabling faster processing and reduced latency. This approach is critical for real-time applications, but it introduces significant challenges in managing resources efficiently in edge-cloud environments. Issues such as increased response times, inefficient autoscaling, and suboptimal task scheduling arise due to the dynamic and resource-constrained nature of edge nodes. Kubernetes, a widely used container orchestration platform, provides basic autoscaling and scheduling mechanisms, but its default configurations often fail to meet the stringent performance requirements of edge environments, especially in lightweight implementations like KubeEdge. This work presents an ILP-optimized, LSTM-based approach for autoscaling and scheduling in edge-cloud environments. The LSTM model forecasts resource demands using both real-time and historical data, enabling proactive resource allocation, while the integer linear programming (ILP) framework optimally assigns workloads and scales containers to meet predicted demands. By jointly addressing auto-scaling and scheduling challenges, the proposed method improves response time and resource utilization. The experimental setup is built on a KubeEdge testbed deployed across 11 nodes (1 cloud node and 10 edge nodes). Experimental results show that the ILP-enhanced framework achieves a 12.34% reduction in response time and a 7.85% increase in throughput compared to the LSTM-only approach.
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TinyML-driven Sensor Nodes for Energy-efficient Acoustic Event Detection in Pervasive Acoustic WSNs
Abstract
The process of sensing and transmitting acoustic signals by pervasive acoustic wireless sensor networks (PAWSNs) poses considerable energy challenges. These problems may be mitigated by filtering only relevant acoustic events from the sensor network. By reducing the number of acoustic events, the frequency of communication may be decreased, thereby enhancing energy efficiency. Although traditional machine learning models are capable of predicting relevant acoustic events by being trained on suitable data sets, they are impractical for direct implementation on resource-limited acoustic sensor nodes. To address this issue, this research introduces TinyML-based acoustic event detection (AED) models which facilitate efficient real-time processing on microcontrollers with scarce hardware resources. The study develops several TinyML models using an environmental dataset and evaluates their accuracy. These models are then deployed in hardware to assess their performance in terms of AED. Thanks to such an approach, only predicted events that exceed a certain threshold are transmitted to the base station via router nodes, which reduces the transmission burden, thus improving energy efficiency of PAWSNs. Real-time experiments confirm that the proposed method significantly improves energy efficiency and boosts node lifetime.
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Reconfigurable Reflectarray Structure Based on Optimized Unit Cell for Wireless Communications
Abstract
This paper presents a 180 × 180 × 1 mm reconfigurable reflector array structure based on an optimized unit cell for wireless communication applications. The reflector array contains 144 unit cells placed on the FR4 substrate, and each unit cell structure uses a single layer based on multi-concentric square rings. The single layer is used to obtain negative εr and μr values, while multiple rings provide a wide reflection bandwidth. The proposed structure is characterized by dual reflection bandwidth. The first band (2.6 GHz) ranges from 1.98 GHz to 4.6 GHz, while the other band (1.71 GHz), ranging from 7.41 GHz to 9.1 GHz. The reconfigurability of the structure is realized by using PIN diodes connected to each unit cell. Phase distribution in the proposed reflector structure changes according to state of the diodes, resulting in the reflection of the wave at different angles. The proposed solution was simulated in terms of S parameters, constitutive parameters and refractive index based on a full-wave analysis performed using CST Microwave Studio.
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Advancing Facial Expression Recognition -- Enhanced MobileNetV3 with Integrated Coordinate Attention and Dynamic Kernel Adaptation
Abstract
This paper presents an improved approach for facial expression recognition (FER), which incorporates the Coordinate Attention (CAM) mechanism into MobileNetV3, a lightweight CNN widely used for its real-time applications on low-power devices. The CA mechanism greatly improves the ability of the model to focus on face regions of interest, as it incorporates positional information, making feature extraction more accurate. Additionally, dynamic kernel adaptation (DKA) and SoftSwish are incorporated into the model to enhance the flexibility and computational efficiency of MobileNetV3. The proposed model was tested in three sets of JAFFE, CK+, and FER2013, where accuracy improvements were reported of 98.84% in the JAFFE dataset, 99.56% on the CK+ dataset, and 88.50% on the FER2013 dataset. These results support the viability and utility of the proposed approach to improve FER, especially in applications that favor higher numerical performance.
