Vol. 101 No. 3 (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.
The articles published in this issue are available under the open access (OA), “publish-as-you-go” scheme. Four issues of JTIT are published each year.
The Journal of Telecommunications and Information Technology is the official publication of the National Institute of Telecommunications - the leading government organization focusing on advances in telecommunications technologies.
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Published:
2025-09-30
Full Issue
ARTICLES FROM THIS ISSUE
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Capon DOD/DOA Estimation Algorithm for Bistatic MIMO Radar Using Dipole Antenna Arrays with Known Mutual Coupling
Abstract
This study focuses on the joint estimation of the direction of departure (DOD) and direction of arrival (DOA) of multiple targets in bistatic multiple input multiple output (MIMO) radar systems employing orthogonal waveforms. A linear array of half-wavelength dipole antennas (HWD) with known mutual coupling is utilized. The proposed method applies a two-dimensional Capon (2D Capon) algorithm to estimate both the DOD and DOA of multiple targets. To mitigate the adverse effects of mutual coupling, an efficient compensation mechanism is integrated into the Capon direction-finding algorithm. This mechanism relies on realistic electromagnetic modeling in which mutual coupling is represented using Toeplitz-structured coupling matrices. Through computer simulations, the influence of various system parameters on the algorithms performance is evaluated, with particular emphasis on its resolution capability and estimation accuracy. The results clearly demonstrate that incorporating mutual coupling compensation significantly enhances the accuracy of the 2D Capon algorithm.
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Evaluating Effectiveness of Implementing G.fast Technology in Ukraine’s Broadband Access Networks
Abstract
The article examines the feasibility of implementing the G.fast technology in the process of modernizing fixed broadband access networks operated in Ukraine. An analysis of international experience in the field and of national broadband development strategies is provided. The data rates achievable by G.fast transmission systems relying on profile 106a over multi-pair TPP and UTP Cat. 5e cables are evaluated, with intrasystem interference and crosstalk taken into consideration as well. The effectiveness of applying the vectoring crosstalk compensation system to increase G.fast transmission rates is assessed. Based on the research results, recommendations are formulated for the effective deployment of G.fast in Ukraine's broadband access networks.
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Physical Layer Security for Keyhole-based NOMA Downlink Systems with a Multi-antenna Eavesdropper
Abstract
This paper investigates the physical layer security of downlink nonorthogonal multiple access (NOMA) systems operating over a degenerate keyhole channel in the presence of a multi-antenna eavesdropper. We propose a joint antenna selection framework with transmit antenna selection at the source and receive antenna selection at both legitimate users and eavesdroppers, thus striving to reduce hardware complexity while maximizing secrecy performance. In this framework, the efficacy of confidentiality is assessed for a specific user allocation methodology by deriving the closed-form approximate expression of secrecy outage probability (SOP). Extensive Monte Carlo simulations validate analytical results and reveal that increasing the number of antennas at the source and legitimate users dramatically lowers SOP, whereas a more capable eavesdropper raises the risk of secrecy. Our findings demonstrate that strategic antenna deployment and non-orthogonal access can effectively safeguard communications even through severely scattering environments.
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Bio-inspired Routing Algorithms for UAV-based Networks: A Survey
Abstract
Rapid technological advancements, exponential growth, and unique characteristics are the key factors that enhance the usefulness of unmanned aerial vehicles (UAVs) in diverse applications, including military, agricultural, commercial, and communications-related fields. The use of UAVs for communication is a recent development that has become a topic of significant interest shown by researchers. A flying ad hoc network (FANET) made up of numerous UAVs cannot be developed without implementing an effective cooperative networking model that enables secure information sharing between UAVs. To achieve reliable and robust communication using FANETs, various design- and routing-related issues must be addressed in an appropriate manner. The use of bio-inspired algorithms for data routing in FANETs may be a promising direction, due to their ability to communicate efficiently in a swarm of devices. This work explores various bio-inspired routing algorithms proposed for transmitting data in UAV-based networks. Furthermore, their performance is evaluated and compared using routing metrics. All unresolved research concerns and prospective study avenues are examined based on the outcomes of the investigation conducted.
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Enhancing Leaf Area Segmentation by Using Attention Gates and Knowledge Distillation in UNet Architecture
Abstract
Accurate segmentation of leaf regions plays a vital role in plant phenotyping and agricultural analysis. This paper presents AKDUNet, a lightweight UNet-based architecture that integrates attention gates and knowledge distillation to improve segmentation performance while minimizing computational complexity. The architecture replaces traditional skip connections with attention gates to focus on salient spatial features and employs a two-stage training pipeline, where a compact student model learns from a deeper teacher model using a tailored distillation loss function. AKDUNet is evaluated on two benchmark datasets (CWFID and Sunflower) and outperforms a range of state-of-the-art models, including UNet++, Inception UNet, VGG-based UNets, SDUNet, INSCA UNet, and SegFormer. Ablation studies confirm the advantages of attention modules, and qualitative analyses using Grad-CAM visualizations reveal the model's ability to effectively focus on crucial leaf structures. The results demonstrate that AKDUNet is not only computationally efficient but also highly accurate, making it suitable for real-time deployment in resource-constrained agricultural environments.
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A Convex Optimization-based Approach for Sidelobe Level Suppression and Null Control in Antenna Arrays by Displacing a Minimum Number of Elements
Abstract
This paper introduces two methods for peak sidelobe level (PSLL) reduction and null steering in the pattern of linear arrays using position control. While most research on this topic uses stochastic optimization techniques, here convex optimization and the off-grid compressive sensing framework were used to accomplish the required goals. For the first method, the problem of minimizing the PSLL and forming prescribed nulls in the pattern of linear arrays by controlling the elements' positions is cast as a convex optimization problem with the help of first-order Taylor approximation. For the second method, the goals are achieved by perturbing the locations of as few array elements as possible. Towards this end, the problem of forming prescribed nulls in the pattern of non-uniformly spaced linear arrays for a predefined PSLL by elements' position control is formulated as a sparse recovery problem within the off-grid compressive sensing framework. Simulations were performed to evaluate the efficacy of the proposed methods, and the results were compared to results obtained using stochastic optimization techniques.
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Optimal Filter Selection for MIMO F-OFDM Systems in 5G Wireless Communication
Abstract
Strong demand for mobile broadband cellular systems has boosted the popularity of emerging high-speed modulation technologies such as multiple input multiple output (MIMO) and cyclic prefix orthogonal frequency division multiplexing (CP-OFDM). However, CP-OFDM suffers from some significant drawbacks in 5G networks, including severe out-of-band emissions (OOBE) and poor spectral efficiency. Filtered orthogonal frequency division multiplexing (F-OFDM) has therefore been found to be a good alternative, as it allows to address these shortcomings by relying on digital filtering to eliminate OOBE and improve spectral efficiency. This study focuses on evaluating the performance of MIMO F-OFDM systems and comparing it with the results achieved by MIMO CP-OFDM, with a particular emphasis placed on reducing spectral leakage and improving overall system performance by using various window functions. Six window types, including Hanning, Hamming, Blackman, root raised cosine (RRC), Nuttall, and Blackman-Harris, are investigated. The research aimed to assess the performance of the system in terms of power spectral density (PSD), peak-to-average power ratio (PAPR), and bit error rate (BER), while using different modulation schemes, i.e. QPSK, 16QAM, 64QAM, and 256QAM, over Rayleigh fading and AWGN channels. Simulation results show that the proposed window filter (Nuttall-Blackman-Hanning) significantly reduces OOBE while maintaining efficient spectral performance. The findings demonstrate that MIMO F-OFDM with the proposed filters achieves better spectral efficiency and reliability, making it a promising candidate for 5G applications requiring high data rates, low latency, and robust signal integrity.
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Performance Optimization of M/M/1 Queues with Working Vacations and Server Breakdowns in Wireless Communication Systems
Abstract
This paper presents a unified analytical and simulation framework for optimizing the performance of M/M/1 queueing systems that incorporate differentiated working vacations, server breakdowns, and customer balking behavior. Other features of the solution include dynamical transitions between full-service mode, two levels of working vacation (with reduced service rates) phases, and random breakdown-repair cycles. Customers arrive via a Poisson process and decide to join or balk based on the server's current state. Embedded Markov chains, probability generating functions, and Matlab based discrete event simulation are applied to analyze key performance metrics, including average waiting time, queue length, and server utilization. A particle swarm optimization (PSO) algorithm is used to identify parameter configurations that minimize congestion and delay. Application scenarios in 5G/6G networks and service platforms demonstrate how adaptive vacation scheduling and resilience strategies improve energy efficiency and throughput. The results offer valuable information for performance tuning in resource-constrained telecommunication systems.
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A Comprehensive Study on Path Loss Estimation Using Deep Hybrid Learning in 5G Networks
Abstract
One of the most important factors in radio network design is path loss - a phenomenon that may be measured using a variety of techniques, including deterministic, empirical, machine learning, and deep learning models. Each approach has its own limitations, such as inability to capture non-linear interactions, high computational resource demand, and inability to reflect changes in environmental conditions, among many others. The deep learning model has the capacity to recognize intricate patterns and has been essential in removing those obstacles; therefore, in this study it is used for path loss prediction in 5G communications in the South Asian region. The model makes use of long- and short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and dense neural network (DNN) approaches to take advantage of all the benefits that each algorithm provides. The performance of the proposed strategy was validated by testing it against multiple state-of-the-art approaches, while relying on the same dataset. An examination of the relevance of characteristics has also been carried out to gain a better understanding of the influence of path loss. A variety of characteristics that are directly related to path loss were evaluated, followed by an examination of how they affect the decision-making process. The results show a possible solution that can help handle this path loss estimation for mmWave communication, especially for 5G networks and beyond.
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Intelligent Secure Data Aggregation in WSNs
Abstract
The paper discusses the problem of secure data aggregation in wireless sensor networks (WSNs) - a procedure that is of critical importance for reducing energy consumption, minimizing transmission overhead, and thus prolonging network lifetime. Due to the limited computational and energy resources of WSN nodes, traditional aggregation methods often fail to perform effectively in dynamic heterogeneous environments. With such a context taken into consideration, this study emphasizes the potential of artificial intelligence techniques, such as neural networks, genetic algorithms, and fuzzy logic, to enable adaptive aggregation approaches tailored to environmental and network-specific parameters. Furthermore, the integration of fuzzy logic, genetic algorithms, and artificial neural networks into a hybrid system leverages the strengths of each approach, resulting in enhanced adaptability and accuracy of the aggregation process. As part of the investigation, a fuzzy inference system (FIS) model was developed that incorporates attributes such as energy, current load, distance to the base station, and trust level. The model was implemented in Matlab using the Fuzzy Logic Designer toolbox. To further improve system performance, a genetic algorithm was applied to optimize membership functions. In the final phase, the model was transformed into an adaptive neurofuzzy inference system (ANFIS) which was trained using simulated data within Matlab. The simulation results demonstrate that the proposed hybrid approach ensures flexible, robust and energy-efficient control of the data aggregation process under dynamically changing conditions in which WSNs operate.
