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. 105 No. 3 (2026)
Explore the newest issue of the JTIT
The scope of modern telecommunications encompasses a wide spectrum of issues: from transmission in wired and wireless systems, through network architecture and information exchange protocols between end systems and network elements (including the protection of communications against unauthorized access), to the subject matter of network services and applications. Across all these areas, one can identify a number of current and significant topics that attract the attention of both researchers and practitioners. The current issue of the Journal of Telecommunications and Information Technology addresses nearly all of these key issues.
We hope you find this issue interesting to read.
Published:
2026-09-30
Full Issue
ARTICLES FROM THIS ISSUE
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Dual Wide-band Microstrip Antenna for Wireless Communication Systems
Abstract
This article presents the design of a compact dual wide-band microstrip antenna. It also describes the simulation of its operation and provides the results of electrical parameter and radiation characteristics measurements. The designed antenna is intended to operate in two ranges. The first covers frequencies from 3.8 to 8 GHz and all the U-NII antenna ranges (U-NII-1, U-NI-2A, U-NII-2B, U-NII-2C, U-NII-3, U-NII-4, U-NII-5, U-NII-6, U-NII-7, U-NII-8) defined in the IEEE-802.11a standard (5.150 to 7.125 GHz). The other covers frequencies from 11.34 to 20.64 GHz and the Ku band (from 11.7 to 12.7 GHz). The performance of the antenna, developed using CST Microwave Studio and Matlab software, was verified through simulation. Then, a prototype was manufactured and was subjected to measurements in an anechoic chamber. Results of the simulation and the measurement campaign were compared against six other dual-band antenna designs available in the literature. Comparison of the parameters of the proposed design and characteristics of other antennas with a similar profile, selected from the literature, shows that the operating bandwidth of the proposed solution with respect to the S11 parameter is the largest, reaching 88.13% for the lower operating band and 59.93% for the upper operating band.
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Machine Learning-based Automatic Modulation Classification for 5G-Advanced and 6G Waveforms: Robust Identification Under Realistic Channel Impairments
Abstract
Automatic modulation classification (AMC) for 5G-Advanced and 6G networks must blindly identify waveforms from received signals under realistic channel impairments, enabling cognitive radio dynamic spectrum access and interference avoidance. No prior work has simultaneously applied machine learning to classify all eight leading waveforms (UFMC, GFDM, FBMC, NOMA, OFDM-IM, OTFS, ODDM, and AFDM) under realistic channel impairments, nor quantified the minimum feature set for resource-constrained deployment.
We present a framework that (i) extracts a 38-dimensional feature vector that includes three novel channel-aware characteristics (amplitude fading variance, phase discontinuity, and frequency drift); (ii) benchmarks nine machine learning classifiers, including an FC-MLP deep learning baseline and five feature selection methods, on 201600 signals across twelve channel conditions (nine custom plus three 3GPP TDL profiles) and seven SNR levels, with leakage-free feature selection; and (iii) identifies a compact 10-feature subset validated with Bonferroni-corrected McNemar tests and Wilson confidence intervals.
FC-MLP achieves 99.09% accuracy; ensemble-boost (99.04%) and random forest (99.02%) are statistically equivalent. The 10-feature random forest reaches a score of 98.90% within 0.12 pp of the full feature baseline at a cost that is 74% lower and with a 0.071 ms inference per block. The five-fold cross-validation confirms stability (98.54%, Wilson 95% CI: 98.49%, 98.59%). Per channel accuracy ranges from 98.87% (Rayleigh) to 99.98% (AWGN/Rician); 3GPP TDL-A/B/C profiles confirm transferability to 5G NR. The three channel-aware features yield up to 3.1% gain under double-selective fading and an average overall improvement. -
Communication-efficient Embedded FFT Processing for Acoustic Telemetry in LPWAN-based Beehive Monitoring Systems
Abstract
Low-power acoustic telemetry remains a significant challenge in Internet of Things monitoring systems deployed in remote environments. This paper presents an embedded fast Fourier transform framework for communication-efficient acoustic monitoring of honey bee colonies. Instead of transmitting raw audio streams, the proposed approach extracts compact spectral descriptors directly on an embedded sensing node and transmits only a small feature vector using a low-power cellular network. The framework is evaluated using labeled queenright and queenless colony recordings. The proposed solution targets resource-constrained ESP32 class IoT nodes operating over LTE-M and NB-IoT networks. The analysis covered such parameters as dominant frequency, peak amplitude, mean spectral amplitude, spectral centroid, spectral entropy, and band energy extracted from the 200 - 400 Hz band. The results showed that dominant frequency alone did not significantly differentiate colony states at the file level, while mean spectral amplitude remained statistically significant. Queenless recordings also exhibited higher dominant frequency variability. The proposed approach reduces the transmitted payload by more than three orders of magnitude while remaining compatible with resource-constrained ESP32 class IoT devices. The results demonstrate that the extraction of embedded acoustic features is a practical method for scalable smart beehive monitoring under strict memory, power, and bandwidth constraints.
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Network Threat Detection in IaaS Environments Based on Flow Log Data
Abstract
The rapid growth of cloud services has led security monitoring to rely mainly on limited telemetry data furnished by cloud service providers. Flow logs, such as VPC Flow Logs in Amazon Web Services, are one of the key sources of information about network traffic in IaaS environments. In this study, a fully automated experimental environment was designed and deployed in the AWS cloud using the infrastructure-as-a-code approach with Terraform. The environment includes a virtual network, flow logging mechanisms, and controlled attack scenarios generating characteristic traffic patterns, such as port scanning, brute-force authentication attempts, and data exfiltration. The collected data was analyzed using cloud-native tools, in particular Amazon Athena, which enabled a detailed investigation of anomaly detection based on flow-level metrics. The results confirm that selected threats can be effectively detected using traffic metadata, including the number of unique destination ports, the repetition of communication attempts, the volume of transferred data and the temporal regularity of flows. The analysis demonstrates that flow logs alone are not sufficient to clearly distinguish between malicious activity and legitimate operations with similar characteristics, highlighting inherent limitations of telemetry in the IaaS model. The paper outlines directions for future work, including correlation with additional log sources and integration with ML models to improve detection accuracy and reduce false positives.
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Adaptive Entropy Prediction-based Lossless Compression for Efficient Data Transmission in Terrestrial and Underwater IoT Systems
Abstract
Demand for quick lossless data compression systems has become more important recently due to the growing deployment of Internet of Things (IoT) devices in terrestrial and underwater environments, under restricted bandwidth, latency and energy consumption conditions. Current dictionary-based and entropy-driven compression methods depend on reactive adaptation and fixed block processing, which makes them less useful in situations where sensing is constantly changing. This paper presents a self-optimizing entropy prediction-assisted lossless compression framework (EP-SLZW), where the compression technique is based on the expected data redundancy and current channel condition. To achieve optimal results, lightweight entropy prediction is employed in combination with adaptive block segmentation and dual dictionary learning between processing and compression. The optimization component of the proposed method enhances the compression strategy by taking care of all important environmental factors to provide the best solutions for radio frequency and acoustic communication channels. The method is evaluated with the help of the MQTT and CoAP protocols through NetSim simulations and hardware experiments. The results show that the proposed approach performs better in comparison to conventional LZW as well as Huffman and run-length encoding techniques, providing better throughput, compression ratio, and less end-to-end delay when dealing with resource-constrained IoT devices.
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A Spatio-temporal Residual Attention Network with SNR-adaptive Weighting for MIMO-OFDM Channel Estimation
Abstract
This paper addresses channel estimation in MIMO-OFDM systems under time-varying Rician fading and spatially correlated propagation conditions, where reliable coherent detection requires accurate channel state information. Although least-squares (LS) estimation is simple and computationally efficient, it remains highly sensitive to noise. In addition, conventional deep learning-based estimators may suffer from tracking bias when temporal channel variations are not properly handled. To overcome these imitations, this paper proposes SMART-CE, a spatio-temporal residual attention network with SNR-adaptive weighting. The proposed approach refines a sequence of LS estimates by extracting spatial-frequency features, exploiting temporal channel correlation, and adaptively balancing historical and current channel information according to the operating SNR. The final channel estimate is obtained through a residual correction of the current LS estimate. Simulation results on an 8 x 8 MIMO-OFDM system show that SMART-CE generally achieves lower NMSE and BER than CNN, residual CNN, residual-attention CNN, recursive least-squares (RLS), and normalized least-mean-square (NLMS) baselines, with the most pronounced gains observed in the low-to-medium SNR regime.
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FEC Hybrid Method Based on Reed–Solomon Codes for Transmitting Video Signal Over Limited Bandwidth and High Loss Rate Channels
Abstract
The paper considers the adaptive forward error correction (FEC) technique for real-time video transmission over a feedback-free channel with limited bandwidth and non-stationary interference. A hybrid Reed-Solomon coding method is proposed in GF(2\textsuperscript{8}), combining differentiated redundancy across specific frame types (I, P, and B), with adaptive code parameter control driven by physical layer channel state estimation. The channel is modeled by a three-state Markov process called Gilbert-Elliott-Jamming (GEJ), in which an explicit jamming state is added to the classical Gilbert-Elliott model. A closed-form expression for the block decoding failure probability under the erasure model is derived, and applicability bounds for representative RS configurations are determined. A redundancy adaptation function with a guaranteed upper bound on overhead is introduced together with a control algorithm. Experiments conducted under four scenarios (stable Wi-Fi, mobile 4G, tactical channel and active jamming) show that the mission success rate increases from 60% for fixed RS and 50% for SRT to 78% under sustained jamming, while the time to first frame decreases from 600 - 1200 ms to 200 ms. The method is implementable on the ESP32-S3 and ARM Cortex-M4 platforms with an encoding throughput of 4 to 8 Mbps.
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Energy-delay Aware Data Collection in Mobile Sink-based Wireless Sensor Networks with Optimized Visiting Points and Two-level Data Aggregation
Abstract
This study addresses data collection challenges in wireless sensor networks (WSNs) employing mobile sinks (MSs). Although MSs improve flexibility and scalability, achieving balanced optimization of data collection delay and energy consumption remains challenging, as minimizing one objective typically comes at the expense of the other. To overcome this limitation, we propose the optimized grid-based data collection with mobile sink (ODCMS) approach. In ODCMS, the MS follows a predefined trajectory and visits strategically selected visiting points (VPs) to collect aggregated data. The number of VPs is minimized to reduce tour length and thus latency, while a two-level data aggregation scheme shortens transmission distances and lowers forwarding overhead, thereby decreasing energy consumption. Energy balancing is ensured through periodic rotation of aggregation roles to prevent early node depletion. The MS movement among VPs and the base station (BS) is modeled as a connected graph, and its trajectory is constructed as a Hamiltonian cycle using a backtracking algorithm. ODCMS also supports rapid reporting of urgent events to the BS prior to MS arrival. NS-3 simulations compared with EDEDA and EFDC demonstrate that ODCMS achieves shorter tour length and data collection delay, lower energy consumption, and longer network lifetime than other approaches.
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Harmonic Trust Spectrum Modeling for Cognitive Radio Intrusion Detection
Abstract
Cognitive radio networks (CRNs) are highly vulnerable to spectrum spoofing, PU emulation (PUE), and harmonic RF manipulation attacks, which significantly degrade dynamic spectrum access reliability and communication security. This paper proposes a harmonic trust spectrum modeling framework for cognitive radio intrusion detection using multi-domain RF intelligence and adaptive machine learning. The proposed system integrates harmonic spectrum analysis, trust oscillation modeling, spectral entropy analysis, wavelet decomposition, and power spectral density (PSD) characterization to detect anomalous RF behaviors. Three heterogeneous RF datasets were used for experimental evaluation: the RadioML 2016.10A modulation dataset, the Oracle RF fingerprinting IQ dataset, normal signals generated by GNU Radio, and malicious PUE RF signals. The proposed methodology extracts advanced harmonic trust features including spectral entropy, spectral flatness, harmonic trust instability index (HTII), and oscillatory trust divergence (OTD), which are subsequently processed using an adaptive XGBoost-based intrusion detection system. Experimental results demonstrate that malicious RF users exhibit significantly higher spectral instability, irregular harmonic distributions, elevated entropy behavior, and oscillatory trust divergence, compared to legitimate users. Furthermore, studies covering fast Fourier transform (FFT) harmonic spectra, PSD analysis, wavelet coefficient analysis, trust oscillation curves, and confusion matrix evaluations confirm the effectiveness of the proposed framework. The proposed harmonic trust spectrum model provides a computationally efficient and scalable solution for next-generation secure cognitive radio communications and RF cyber defense systems.
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Two-way Planar Array Radar with Diamond- and X-shaped Configurations for Sidelobe Suppression
Abstract
The undesirable sidelobes in the pattern of a two-way radar antenna array is usually minimized by thinning several elements in the receive or transmit arrays. The thinning process is performed randomly on all array elements by using an optimization algorithm, with the computation time being significant due to a large number of combinations that need to be examined. In this paper, sidelobe suppression in a two-way array pattern is achieved by properly structuring the received array's elements into a diamond- or X-shape, such that its far-field pattern has exact null regions toward the directions of the sidelobe regions of the transmit array pattern. Then, multiplication of the null regions of one pattern with the sidelobe regions of another pattern results in a great reduction in the sidelobe levels of the resultant two-way array pattern. The transmit array structure was fixed as a standard rectangular shape, which has two dominant regions of the sidelobes along the horizontal and vertical lines on the array aperture. To obtain ultra-low sidelobes, the receive array structure should have nulls on these lines, and their sidelobe regions should be located on the diagonals of the array aperture. To meet these alignment conditions, the received array structure was designed as a diamond- or X-shaped structure. It was observed that the proposed configurations offer potential to suppress the sidelobe levels to more than -51 dB and the complexity ratio was lowered to 43%, with both values being much lower than the ones available in the literature.
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An Adaptive Threshold Tracking Area Update and Selective Probabilistic Paging Scheme for Signaling Cost Reduction in 5G NR Networks
Abstract
Tracking area update (TAU) and paging are two competing signaling-cost characteristics of 5G NR networks: a smaller registration area reduces paging cost but increases TAU signaling and vice versa. The majority of existing schemes optimize one parameter while fixing the other and apply a single network-wide parameterization to all users regardless of their heterogeneous call and mobility patterns. This paper proposes ATAU-SP, a scheme that manages both operations jointly, on a per user equipment (UE) basis: each UE maintains an online estimate of its call-to-mobility ratio (CMR) and triggers a TAU only after an adaptive movement threshold derived by minimizing a per UE cost function, with a dwell-time guard that suppresses boundary ping-pong updates. The network builds a residence-probability distribution from a learned Markov mobility model and pages cells in descending probability rounds up to a delay bound. We derive a closed-form analytical cost model and validate it as a provable upper bound against a custom discrete-event simulator. The results show that a well-tuned TAL scheme is the lowest-cost baseline over most of the moderate CMRs, ATAU-SP-ML attains the lowest cost at high CMR, and plain ATAU-SP is best only at very low CMR. Nonetheless, ATAU-SP variants dominate the classical static, distance-, movement-, and time-based schemes throughout, and both ATAU-SP and TAL require materially less machinery per UE state and network side than a full TAL list distribution subsystem.
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Explainable Network Traffic Classification Using XGBoost and SHAP: Interpreting Feature Contributions Across Traffic Classes
Abstract
Network traffic classification has become an essential component of modern network management, cyber security, and quality-of-service provisioning. The widespread adoption of encryption technologies has reduced the effectiveness of traditional traffic identification techniques, leading to the use of ML approaches based on flow-level characteristics. Although several machine learning and deep learning models were evaluated, the black-box nature complicates the practical deployment in security critical environments. This research proposes a framework for ML-based network traffic classification of explainable artificial intelligence (XAI). The CIC-Darknet2020 dataset is divided into four classes: non-TOR, non-VPN, TOR and VPN. Additionally, machine learning algorithms, including ID3, k-nearest neighbors (KNN), random forest, CatBoost, XGBoost, and LSTM are used as a deep learning approach. The evaluation is carried out through stratified split of trains and 10-fold cross-validation, while XGBoost is classified for explainability analysis. By ensuring model transparency, SHAP (SHapley Additive exPlanations) identifies the most influential features contributing to classification predictions. Furthermore, a novel category SHAP analysis is introduced by grouping higher-level behavioral categories, including temporal, statistical, rate-based, and TCP-related features. The results revealed that temporal traffic characteristics and transport layer behavioral features influence classification outcomes, particularly for encrypted traffic classes. Moreover, the framework demonstrates that high classification performance and model interpretability are achievable simultaneously within a category-level study that enhances the transparency, trustworthiness, and practical applicability of machine learning-based network traffic classification systems.
