No. 4 (2024)
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:
2024-12-31
Full Issue
ARTICLES FROM THIS ISSUE
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Efficient Radio Resource Management in Cell-less Wireless Communication Systems
Abstract
In this paper, the particle swarm optimization (PSO) method with dynamic generation of biasing factors is used to determine the optimal particle size, maximize cell spectral efficiency (CSE) and balance the load in 5G networks. This work studies two distinct interference scenarios: in the first approach, CSE is calculated with varying numbers of users, when different radio services are used by each tier (when several radio access technologies are used), and when interference is received by the consumer only from the same tier base stations (BSs). In the second approach, interference is created when all levels use the same radio services and interference from BSs belonging to the same tier and other tiers is received by the consumer. Simulation results show that the cell-less network performs better than the cellular network in terms of maximizing CSE and balancing the load.
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Compact All-metal In-line Combline Coaxial Cavity Diplexer
Abstract
This article describes the design of an all-metal combline coaxial cavity diplexer. The device is based on a Y-shaped star-resonant junction which allows to achieve a compact design by positioning the two channels in an in-line and side-by-side arrangement. The channels share the same geometry and are tuned to resonance using screws. The device was designed using the coupling matrix method. For verification, a combline cavity diplexer was manufactured and tested for E1 Galileo (1559-1591 GHz) and Iridium (1606-1638 GHz) applications with fractional bandwidth equaling 2% for both channels. The order and the return loss of each channel are 5 and 19.4 dB, respectively. The volume is 154.1 × 36 × 27.8 mm³, corresponding to a normalized volume of 0.810 × 0.189 × 0.146 λ³. The normalized volume per resonator is as low as 0.0047 λ³, while isolation is better than 55 dB. The ratio between the unloaded quality factor and the normalized volume per resonator is as high as 19.8 × 10⁴ λ⁻³. The design is very easy to manufacture, since it is all-metal and has a simple geometry.
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Realistic Approach to Description of Signals at Output of A/D Converters
Abstract
It is common knowledge that no signals having the form of a sequence of weighted Dirac deltas, as they are currently modelled so, are present, in reality, at the output of analog-to-digital converters (ADC). No such signals are available in the otherwise large variety of physical signals. This has been illustrated in many works and shown by means of measurements. However, it is highly intriguing that the same papers (with the exception of this article) consider it necessary to model the signals at the output of ADC via sequences of Dirac deltas because only such an approach allows to describe the aliasing effect occurring in the spectra of these signals. But due to this incorrect description such an approach is obviously incomprehensible to everyone. In accepting this, however, it has been overlooked that this does not have to be the case at all. The effect of aliasing in the spectrum of a sampled signal may be explained by modeling this signal as a weighted step function. Moreover, such an approach offers also a quantitatively more accurate result for this spectrum than the one obtained using the current method. All that is illustrated in this paper that focuses on this specific theme.
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Non-uniformly Spaced Antenna Arrays with Overlapped Elements Constraint
Abstract
In the literature, inter-element spacing antenna design methods have been widely discussed and presented as an alternative approach to element excitation amplitude and/or phase control methods that may be relied upon to achieve the required array pattern shapes. However, methods associated with non-uniformly distributed elements suffer from the element overlap problem, where some of the optimized element locations may overlap each other and cause changes in the overall array aperture length. Practically, these element overlaps cannot be implemented, due to the physical antenna element size, without omitting some of them. Consequently, the overall performance of the antenna array is degraded. Further, degradation may occur when considering phased arrays with scanned main beams. In this paper, we first illustrate the effect of the problem of overlapped element locations and then we propose two approaches based on the genetic algorithm to optimize non-uniformly spaced arrays with overlapped element locations, while simultaneously preserving the array's directivity. To solve the problem of overlapping and to determine the physical array element size, the minimum element-spacing constraints are incorporated in a simple way in the proposed approaches. Thus, the time required to perform optimization-related computations is greatly reduced. Simulation results confirm the effectiveness of the two proposed solutions, where the probability of the elements overlapping has been reduced to zero under specific conditions related to the locations of the some of the elements, while the peak sidelobe levels were always kept below -15 dB and directivity was maintained, to the extent possible, at the level of that of standard uniformly spaced arrays.
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Enhancing Biometric Security with Bimodal Deep Learning and Feature-level Fusion of Facial and Voice Data
Abstract
Recent research in biometric technologies underscores the benefits of multimodal systems that use multiple traits to enhance security by complicating the replication of samples from genuine users. To address this, we present a bimodal deep learning network (BDLN or BNet) that integrates facial and voice modalities. Voice features are extracted using the SincNet architecture, and facial image features are obtained from convolutional layers. Proposed network fuses these feature vectors using either averaging or concatenation methods. A dense connected layer then processes the combined vector to produce a dual-modal vector that encapsulates distinctive user features. This dual-modal vector, processed through a softmax activation function and another dense connected layer, is used for identification. The presented system achieved an identification accuracy of 99% and a low equal error rate (EER) of 0.13% for verification. These results, derived from the VidTimit and BIOMEX-DB datasets, highlight the effectiveness of the proposed bimodal approach in improving biometric security.
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A New Tree Quantum Key Agreement Protocol for Secure Multiparty Communication
Abstract
The Tree Multiparty Quantum Key Agreement Protocol (TMQKAP) is introduced as a novel solution for secure quantum key agreement among multiple participants, specifically tailored for tree topologies. Based on the BB84 protocol, TMQKAP employs hierarchical tree structures and XOR operations to facilitate efficient and secure key generation. Key elements are exchanged among participants in an equitable manner, ensuring that each participant contributes equally to the generation of the shared key. The protocol demonstrates robust security, effectively defending against both external and internal attacks, and achieves a quantum efficiency of 1/2 (N −1), where N is the number of participants. Thorough security analysis and simulations show TMQKAP’s robustness against various attacks while maintaining high efficiency. Additionally, the protocol is readily implementable with current quantum technologies, utilizing single-photon transmission to facilitate secure key distribution.
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Metamaterial-based Luneburg Lens for RF Applications Using Additive Manufacturing
Abstract
This article takes a detailed look at modeling, simulating, calculating, and fabricating a Luneburg lens using a single material and advanced 3D printing technology. The Luneburg lens is a type of gradient index lens that is spherically symmetrical, which simplifyies its manufacturing process and enhances its structural stability. However, fabrication may be expensive due to the special materials required for manufacturing. Discovering simpler and cost-effective production methods would enable the wider use of Luneburg lenses across various fields. The objective of this study was to use the lens to increase the gain and directivity of antennas at 5.8 GHz while maintaining a compact lens size and using low-cost material, such as ABS-like filament. A single-cell cross-shaped structure was utilized to construct the lens using 3D printing technology.
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Development and Optimization of Deep Learning Systems for MRI Analysis in Alzheimer's Disease Monitoring
Abstract
Alzheimer's disease is one of the leading causes of dementia worldwide, and its increasing prevalence presents significant diagnostic and therapeutic challenges, particularly in an aging population. Current diagnostic methods, including patient history reviews, neuropsychological tests, and MRI scans, often fail to achieve adequate sensitivity and specificity levels. In response to these challenges, this study introduces an advanced convolutional neural network (CNN) model that combines ResNet-50 and Inception V3 architectures to classify, with a high degree of precision, the stages of Alzheimer's disease based on MRI. The model was developed and evaluated using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and classifies MRI scans into four clinical categories representing different stages of disease severity. The evaluation results, based on the precision, sensitivity and F1 score metrics, demonstrate the effectiveness of the model. Additional augmentation techniques and differential class weighting further enhance the accuracy of the model. Visualization of results using the t-SNE method and the confusion matrix underscores the ability to distinguish between disease categories, supporting the model's potential to aid in neurological diagnosis and classification.
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Leveraging Digital Maps to Visualize Data in Doppler Effect-based Localization System Relying on GNSS
Abstract
This paper presents a localization solution exploiting the Doppler effect, digital maps and the Global Positioning System (GPS). To deploy such a system, the following steps must be completed: selecting a suitable GPS receiver, developing operating software, creating an app for displaying digital maps offline, choosing a software-defined RF receiver with a stable frequency reference, integrating the GPS receiver with the map and a radio within a software environment, and setting up a transmitter-receiver link. The second part of the research involves comprehensive tests of the integrated localization system and analyzing the empirical results obtained. The novel approach described in this article consists in the use of digital maps and GNSS data for dynamic visualization of transmitter location using the SDF method. The research was carried out in an NLOS environment.
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Efficient Routing for Delay-energy Tradeoff in Event-based Wireless Sensor Networks
Abstract
Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT) by providing a foundation for collecting, transmitting and processing data from the physical world. Beyond the necessity of proposing solutions that are in line with the constrained resources of sensor nodes, particularly their limited energy capacity, the consideration of real-time data collection becomes essential. This is particularly vital due to the fact that many IoT applications require timely data collection. However, the need to establish energy-efficient routes contradicts the requirement to guarantee timely data collection. Hence, achieving an equilibrium and striking, subsequently, a trade-off between these two issued becomes imperative. To answer this question, a localized delay-bounded and energy-efficient routing protocol (abbreviated as LDER) is presented. It is based on another protocol, namely DEDA, aimed at achieving a higher energy conservation degree. To validate the efficacy of LDER, simulations were conducted using the J-sim simulator. The results demonstrate the ability of LDER to achieve the desired equilibrium and prove its superiority over DEDA.
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Optimizing Cognitive Radio Networks with Deep Learning-Based Semantic Spectrum Sensing
Abstract
Spectrum aggregation in 4G and 5G networks is a technique used to combine multiple frequency bands to boost communication performance. The cognitive radio feature improves the ability to combine spectrum in LTE and 5G environments by enabling dynamic spectrum sensing. Spectrum sensing is a major problem in spectrum aggregation due to the presence of various types of interference, such as noise. Phase noise is an issue due to its 1 MHz frequency offset experienced within 5G's 28 GHz operating band, with the distorted signal generating more spectrum sensing-related errors. To solve this problem, the proposed work suggests an optimized deep learning-based semantic spectrum sensing model using three sets of optimizers (ResNet-50, DeepLab V3 and sand cat) offering a high detection accuracy of 99.7% with the optimized training parameter of a high signal-to-noise ratio equaling 40 dB.
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Efficiency and Fairness Optimization in Energy Management
Abstract
The paper proposes a solution to the problem of distributing electricity originating from various sources. In the proposed model, each source has a different cost of acquisition and is characterized by varying energy efficiency factors. Additionally, in the case of renewable sources, the costs of storing energy are taken into consideration as well. This work presents a fair and cost-efficient approach to distributing the demands of energy providers. A model has been developed and verified for the purpose of corroborating the process.
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Examination of 5G NR, LTE, and NB-IoT Radio Interfaces and Their Vulnerabilities to Interference
Abstract
Modern cellular wireless communication systems of the fourth (4G) and fifth generation (5G) face a problem of various types of interference or intentional jamming. Consequently, a degradation of the services provided and an incorrect network operation may occur. In this paper, configuration of the networks' physical layer is investigated, with the said investigation preceded by the measurement of parameters of commercial networks operating in two different environments, to assess their vulnerabilities to interference or intentional jamming. Finally, a method for analyzing the radio signal received with the use of 5G New Radio (NR), Long Term Evolution (LTE), and Narrowband Internet of Things (NB-IoT) radio interfaces is proposed, to detect and mitigate the negative impact of unwanted signals. Software-based implementation of the proposed method allows one to detect and mitigate co-channel interference, intentional jamming and maintain compatibility of user equipment (UE) with the 3rd Generation Partnership Project (3GPP) standard, as it does not affect operations performed, for instance, at the time and frequency synchronization or channel parameter estimation phases.
