About the Journal
The Journal of Telecommunications and Information Technology is published quarterly. It comprises original contributions, dealing with a wide range of topics related to telecommunications and information technology. All papers are peer-reviewed. The articles presented in JTIT focus primarily on experimental research results advancing scientific and technological knowledge about telecommunications and information technology.
Current Issue
Vol. 103 No. 1 (2026)
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:
2026-03-31
Full Issue
ARTICLES FROM THIS ISSUE
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Outage Probability in RIS-assisted LoRa Networks with Hardware Impairments and Asymmetric Channels
Abstract
This paper investigates the performance of reconfigurable intelligent surface (RIS)-assisted LoRa networks. Specifically, we consider a LoRa system enhanced by RIS under the influence of hardware impairments and asymmetric channel conditions. A closed-form expression for the outage probability at end devices is derived using the method of moments. The accuracy of the proposed analytical framework is extensively validated through Monte Carlo simulations. Several important insights are drawn from both the theoretical analysis and simulation results. In particular, the system's performance is significantly enhanced by an increase in the number of RIS elements and the transmission power of the gateway. Furthermore, comparisons with related works described in the literature are made to show that the proposed system outperforms these existing approaches simply by increasing the number of RIS elements. Additionally, we reveal that a higher spreading factor (SF) does not necessarily lead to worse performance than a lower SF, and the impact of hardware impairments is found to be minor under typical operating conditions.
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Optimizing Circular Arrays with Concentric Subarray Rings for Wireless Power Transmission Applications
Abstract
The emerging wireless power transmission technology creates new opportunities in numerous real-world applications such as wireless charging systems, robots, and aerospace solutions. This paper introduces an optimized method for designing transmit antenna arrays which may be used for long-distance wireless power transmission with narrow focusing of RF power on remote receivers. The novelty of this paper consists in using an effective clustered subarray rings configuration with a transmit circular array instead of its conventional full aperture array, based on the configuration of individual elements. The final goal is to obtain simpler, cheaper, and lighter arrays. Amplitude and phase excitation weighting as well as the number of elements in each clustered subarray are optimized jointly to maximize the efficiency of transferring power to a target region while minimizing sidelobe powers outside the intended region. The simulation results show that the beam collection efficiency of the proposed system with 21 subarray rings was 98.99%, while that of the conventional circular array with individual dense elements of size 21×21 equaled 99.68%.
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Comparative Analysis of Classifiers for Higher-order Statistics-based Modulation Recognition in Cooperative STBC-OFDM
Abstract
Precise classification of modulation in cooperative relaying networks remains challenging in the presence of carrier frequency offset (CFO) and imperfect channel state information (CSI). This paper conducts a comprehensive comparative analysis of automatic modulation classification (AMC) methods for distributed space-time block-coded orthogonal frequency division multiplexing (DSTBC-OFDM) systems under these impairments. A unified simulation framework is developed that combines pilot-assisted CFO and CSI estimation with higher-order statistics (HOS)-based feature extraction. Four widely used machine learning classifiers, i.e. feedforward neural network, support vector machine, random forest classifier, and adaptive boosting, are benchmarked under identical channel and noise conditions. Monte Carlo simulations are performed across varying SNR levels and fading scenarios, enabling a fair assessment of classification accuracy, robustness to residual estimation errors, and relative computational complexity. The results provide practical insights into the strengths and limitations of each classifier in cooperative STBC-OFDM environments, offering valuable guidelines for selecting AMC techniques in future cooperative wireless systems.
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Priority-aware Radio Resource Scheduling for mMTC in 5G Networks - Balancing Efficiency and Fairness
Abstract
Efficient and fair resource allocation for massive machine-type communication remains a significant challenge in 5G New Radio networks due to the diverse quality of service requirements and dynamic traffic patterns. This paper proposes a priority-aware uplink scheduling (PAUS) algorithm that jointly considers channel quality, 5G QoS identifier, packet aging, and fairness in physical resource block allocation, while simultaneously mitigating starvation of low-priority user equipment. The algorithm utilizes a composite fitness function to implement binary integer optimization for uplink scheduling, supported by heuristic resource assignment to ensure scalability. Simulation results demonstrate that the PAUS algorithm achieves an improved balance between throughput, resource utilization, delay, priority satisfaction, and fairness compared to baseline schedulers with polynomial-time complexity.
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A Spectral Efficiency Design for Active IRS-assisted SWIPT System via Semidefinite Relaxation Method
Abstract
Active intelligent reflecting surfaces (IRS) with phase-shift and amplifier capabilities have arisen as a solution relied upon to improve spectral/energy efficiency of wireless systems, as they outperform conventional passive techniques/without IRS assistance. In this work, the simultaneous wireless information and power transfer (SWIPT) downlink is supported by an active IRS, where a multi-antenna base station (BS) broadcasts both information and power to multiple hybrid power-splitting (PS) users. The target of sum data rate maximization is to study the constraints of user energy harvesting thresholds and power transmission limitations of BS and active IRS. To tackle this complicated issue, iterative algorithms are proposed to find the optimal beamforming vector, PS coefficients, and IRS parameters, as amplification factors and phase shift. A joint optimization framework using alternating optimization, semidefinite relaxation, and non-convex approximations is used. Finally, simulation experiments are performed to assess that the proposed iterative algorithms of the active IRS scheme converge fast and achieve better sum rate results than conventional baseline schemes.
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Determining Speed and Reliability of Transmitting Indicator Information in Residual Class Systems
Abstract
The article is devoted to the analysis and assessment of the efficiency of fiber optic systems whose primary objective is to transmit data. The efficiency of information transmission systems depends on numerous indicators, such as interference immunity, speed, energy efficiency, cost, development time, and design. However, from the user's point of view, quality of service is determined primarily by transmission speed and reliability. This article compares two systems. The first corresponds to the modern paradigm: one user - one transmission channel. In the other, the number of users is provided with a complex channel for transmitting symbols of the alphabet of a certain system of residual classes. At the same time, the transmission speed in the residual class system - compared to the classical multiplexing method - decreases slightly to 28/32, while the reliability (determined based on the probability of failures) increases by several orders of magnitude. The work proves a lemma that determining the optimal alphabets of residual class systems allows to optimally approximate the transmission speed of modules to binary coding systems. An analysis of the non-linear loss function, which considers the speed parameters and probabilistic reliability indicators, is performed as well.
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DOA Estimation of Linear Dipole Arrays Based on Horse Herd Optimization Algorithm
Abstract
Subspace-based direction of arrival (DOA) estimation algorithms, such as MUSIC and ESPRIT, are designed for adaptive smart antenna arrays. However, these subspace methods require a large number of signal snapshots and sufficient angular separation between signals to provide an accurate DOA estimation of RF signal sources. Moreover, their resolution degrades significantly in severe noise scenarios. This study proposes a swarm intelligence (SI) algorithm, known as horse herd optimization (HOA), to address these limitations. An optimizer is employed as a direction-finding method to estimate the directions of arrival (DoAs) of incident signals impinging on a linear array of half-wavelength dipole (HWD) antennas by examining the global minimum of a non-linear cost function. This cost function is defined as the difference between the actual and estimated angles and is used to evaluate candidate solutions. Simulation results of the proposed algorithm have been compared with other recognized algorithms, including ESPRIT, root-MUSIC, and PSO, to verify estimation accuracy, convergence behavior, robustness against the number of elements, noise, and snapshots over Monte Carlo trials. It has been observed that the suggested HOA achieves better performance with a few snapshots, outperforms PSO and subspace-based methods when it comes to estimating DOA of incoming signals, particularly in a low signal-to-noise ratio (SNR) environment, and even when only fewer snapshots are available.
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ANN-Enabled Gain Prediction and Optimization in Dual-Band SIW Antenna Designs for 5G Networks
Abstract
Artificial neural networks (ANNs) help improve antenna design process by enabling adaptive optimization strategies that address important challenges in 5G wireless systems, including signal interference, limited coverage, and high user density. This study presents an AI-assisted design methodology for a compact dual-band substrate integrated waveguide (SIW) antenna optimized for 5G operation at 28 and 38 GHz. The antenna is implemented on a Rogers RT/Duroid 5880 substrate using a novel slot configuration with strategically positioned vias to enhance radiation characteristics. The fabricated prototype achieves gains of 8.05 dBi at 28 GHz and 7.89 dBi at 38 GHz, with fractional bandwidths of 6.41% (27.491 - 29.277 GHz) and 1.81% (37.496 - 38.179 GHz), while maintaining a return loss below -10 dB across both operating bands. The pivotal contribution of this work is the development of an ANN-based predictive model capable of accurately estimating antenna gain and radiation efficiency from main parameters such as slot dimensions, via size and feedline width. The proposed model demonstrates excellent predictive accuracy, achieving mean squared error values in the range of 0.00 to 0.001 for gain prediction and 0.018 to 0.066 for radiation efficiency estimation. This AI-driven framework significantly reduces design iterations, computational overhead, and prototyping requirements, offering an automated framework for efficient antenna development in next-generation 5G communication networks.
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An Adaptive Video Data Representation Model to Increase Delivery Efficiency in Next-Generation Networks
Abstract
This study proposes an adaptive video stream representation model that provides dynamic adjustment of bitrate, frame rate, compression ratio, and frame structure based on a comprehensive analysis of network conditions, content priorities, and technical features of endpoint equipment. The research methodology includes mathematical modeling of video data transmission processes, analysis of radio channel noise immunity, algorithmic formalization of adaptive optimization of video parameters, and simulation modeling in Wi-Fi, 4G/5G, and PON networks. The results show that the proposed model provides a bandwidth reduction of 22 - 30% compared to static coding and classical ABR algorithms, reduces buffer time by 40 - 60%, increases delay stability to 150 ms in 5G and to 300 ms in 4G networks, and decreases packet loss rate to 1 - 3%. Its PSNR and SSIM metrics remain stable and device load is reduced by 15 - 20%.
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Iterative Beamspace Covariance Refinement for Precise DOA Estimation in Uniform Circular Arrays under Low-snapshot Conditions
Abstract
Uniform circular arrays (UCA) provide 360° angular coverage and uniform directional response, making them preferable for direction-of-arrival estimation (DOAE). This paper proposes an iterative enhancement technique for root-MUSIC-based methods under UCA configurations, particularly effective in low-snapshot scenarios. The idea is to iteratively refine the beamspace sample covariance matrix (BSCM) by estimating and suppressing residual components that alter the signal and noise subspaces. This refinement significantly improves the accuracy of DOAE, even under limited data conditions. Numerical simulations demonstrate that the proposed method outperforms conventional UCARM, sparse UCARM, UCARBRM and unitary UCARM algorithms in terms of estimation error, beamspace leakage, conditional mean square error (CMSE) and resolution probability - across uncorrelated and correlated signal scenarios. The proposed technique is also applicable to RARE-based approaches for 2D DOAE, while preserving the beamspace covariance structure. Furthermore, the proposed method is suitable for electronic surveillance systems and low-power sensor networks.
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Enhancing Data Transmission Security and Reliability of OFDM-IM for 5G Wireless Communication Systems
Abstract
Thanks to its improved spectral efficiency and immunity to frequency selective fading, OFDM with index modulation (OFDM-IM) has become a perspective option. Unfortunately, OFDM-IM systems are vulnerable to security risks due to their inherent openness encountered in wireless communications. Conventional encryption techniques, which focus on the upper layers, add complexity and might not be enough to fend off malicious attacks. To improve the selection of subcarrier indexes and modulation of data symbol modulation, this work proposes a new chaotic encryption approach for OFDM-IM systems that uses Lorenz chaotic maps. Comprehensive simulations show that, in comparison to traditional methods, the proposed approach provides better security against eavesdropping while maintaining transmission reliability.
