No. 3 (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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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:
2024-09-30
Full Issue
ARTICLES FROM THIS ISSUE
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Testing Time Optimization Method for IEEE 802.15.4z Ultra-wideband Integrated Circuits
Abstract
IEEE 802.15.4z-compliant ultra-wideband (UWB) devices are becoming ever more popular in contemporary radio engineering systems. Such systems are capable of precisely measuring distances (with their accuracy expressed in centimeters), are immune to interference, offer low latency and transmit data in an energy-efficient manner. Widespread adoption of UWB technology has triggered significant demand for testing integrated circuits these systems rely on, prompting the development of new testing methods to meet the ever increasing requirements in terms of testing speed and reliability. The same applies to sensitivity tests, in the course of which up to 2000 different packets may be received. The process of generating and analyzing such a large number of packets is time consuming. Furthermore, if multiple devices need to be tested simultaneously, the duration of the test will be multiplied accordingly. In such a context, the article investigates the lead time required to generate 2000 UWB packets using conventional methods and proposes a novel approach to significantly reduce packet generation time and improve testing efficiency.
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Performance Analysis of Antenna-relay Selection in CNOMA Systems under Practical Impairments
Abstract
Selection strategies prove to be a valuable approach in mitigating complexity associated with antenna selection (AS) and relay selection (RS), optimizing signal transmission through a streamlined number of antennas/relays, and enhancing overall system performance. This paper offers a comprehensive analysis, deriving closed-form expressions for the outage probability (OP) and throughput in proposed scenarios that leverage the best relay selection (BRS) and transmit antenna selection (TAS) protocol for cooperative non-orthogonal multiple access (CNOMA), along with partial relay selection (PRS) and TAS protocol for CNOMA. The study extends to Rayleigh fading channels, considering practical impairments such as successful interference cancellation (SIC) error, channel estimation error (CEE), and feedback delay error. In comparing the proposed system to conventional CNOMA, our findings highlight the substantial impact of SIC, CEE, and feedback delay imperfections on the performance of both proposed scenarios. Notably, the application of BRS-based TAS protocol outperforms PRS-based TAS in terms of OP and throughput. The close alignment between analytical, asymptotic, and simulation results attests to the credibility of conducted analysis.
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Multiprobe Planar Near-field Range Antenna Measurement System with Improved Performance
Abstract
This article presents a novel multiprobe planar near-field range (PNFR) measurement system. The said system simplifies the overall mechanical design, making it simpler than the existing scanning probe PNFR measurements, and also significantly reduces testing time. A dielectric-based probe is introduced to reduce the antenna size, thereby improving resolution. The probe under consideration is an antipodal Vivaldi antenna offering broadband support and ensuring wideband characteristics of aerials. Numerical results for representative X-band antenna models, presented in the Matlab environment, demonstrate robust performance of the developed measurement system.
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Analyzing Performance of THz Band Graphene-Based MIMO Antenna for 6G Applications
Abstract
In this paper, a compact 2×2 hexagon ring-shaped MIMO antenna operating at the terahertz band is proposed for future 6G wireless communication applications. The antenna is designed using graphene, due to its unique high-speed transmission capabilities. DGS and NL decoupling approaches are applied to enhance isolation between the two radiating elements. A parametric study is performed to investigate the significance of using these methods. Performance in terms of different metrics is studied using the CST Microwave Studio simulator. The final outcomes show that the proposed MIMO antenna achieves 23 dB of isolation, 0.004859 of ECC, 0.004 bits/sec/Hz of CCL, and efficiency of 98%.
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Increasing Parallelism in Forward-backward Distributed Algorithm for Finding Strongly Connected Components of Directed Graphs
Abstract
The paper proposes a modification of the existing distributed forward-backward algorithm for finding strongly connected components in directed graphs. The modification aims at improving the parallelism of the algorithm by increasing the branching factor while dividing the workload. Instead of randomly picking the pivot vertex, a heuristic technique is used which allows more sub-tasks to be generated, on average, for the subsequent step of the algorithm. The work describes suitable algorithm modifications and presents empirical results, proving suitability of the approach in question.
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A Generalized Learning Approach to Deep Neural Networks
Abstract
Optimization of machine learning architectures is essential in determining the efficacy and the applicability of any neural architecture to real world problems. In this work a generalized Newton's method (GNM) is presented as a powerful approach to learning in deep neural networks (DNN). This technique was compared to two popular approaches, namely the stochastic gradient descent (SGD) and the Adam algorithm, in two popular classification tasks. The performance of the proposed approach confirmed it as an attractive alternative to state-of-the-art first order solutions.
Due to the good results presented in the case of shallow DNN, in the last part of the article an hybrid optimization method is presented. This method consists in combining two optimization algorithms, i.e. GNM and Adam or GNM and SGD, during the training phase within the layers of the neural network. This configuration aims to benefit from the strengths of both first- and second-order algorithms. In this case a convolutional neural network is considered and its parameters are updated with a different optimization algorithm. Also in this case, the hybrid approach returns the best performance with respect to the first order algorithms. -
A Review of Isolation Techniques for 5G MIMO Antennas
Abstract
This paper offers an analysis of mutual coupling reduction techniques used in MIMO antennas designed for sub-6 GHz, 28 GHz, and 28/38 GHz dual frequency bands which are allocated to 5G technology. The said techniques take into account size, gain, isolation, and all diversity-related parameters, such as envelope correlation coefficient (ECC), directive gain (DG), and channel capacity loss (CCL). A review of current technologies is presented in the paper too. The isolation techniques are studied in detail and comparisons between the various works are drawn. Finally, the best isolation technique suitable for specific bands, applications and different port numbers is determined.
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High-isolation Quad-port MIMO Antenna for 5G Applications
Abstract
This paper presents a compact, high-isolation MIMO antenna with physical dimensions of 68 × 68 × 0.8 mm, designed for use in 5G applications. The antenna's bandwidth ranges from 3.25 GHz to 4.34 GHz and it offers a gain of 4.3 dBi, making it suitable for applications relying on 5G technology. Several improvements have been introduced to improve its overall efficiency, such as adjustments to the ground plane and integrating apertures in the radiating patch. The alterations referred to above were optimized using the sweep parameter method to ensure that their best configurations are achieved. Furthermore, much attention has been paid to enhance isolation by ensuring all terminals are positioned precisely at 90° angles. The CST Studio Suite was utilized to design and thoroughly simulate the proposed MIMO antenna.
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A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems
Abstract
This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.
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TS-based Mobility-aware Multi-hierarchical Caching Model with Vehicle Clustering and Content Popularity Prediction
Abstract
This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments.
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A Collaborative Approach to Detecting DDoS Attacks in SDN Using Entropy and Deep Learning
Abstract
Software-defined networking (SDN) is an approach to network management allowing to enhance the performance of the network and making it more flexible. The centralized architecture of SDN makes it vulnerable to cyberattacks, especially distributed denial of service (DDoS) attacks. Existing research investigates the detection of DDoS attacks separately on the control plane and data plane. However, there is a need for efficient and accurate detection of these attacks using features obtained from both control and data planes. Therefore, we present a mechanism for identifying DDoS attacks using entropy, multiple feature selection mechanisms, and deep learning. Initially, we use entropy on the control plane to detect anomalous activity and identify suspicious switches. Next, we capture traffic on the suspicious switches to detect DDoS attacks. To detect these attacks, we utilize multi-layer perceptron (MLP) deep learning models, convolutional neural network (CNN), and the long short-term memory (LSTM) approach. An InSDN dataset is used to train the model and test data are generated using Mininet emulation and the Ryu controller. The results reveal that LSTM outperforms MLP and CNN, achieving an accuracy of 99.83%.
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Achieving Reliability of Privacy-preserving Phantom Routing Protocols in Multi-hop Wireless Sensor Networks
Abstract
Due to the open nature of wireless channels and sensor node resource constraints, it is challenging to secure the communication in wireless sensor networks (WSNs) while simultaneously protecting the privacy of node location data. Therefore, a significant amount of research focusing on source location privacy (SLP) protocols has been conducted. The amount of research on SLP reliability, meanwhile, is insignificant. This study explores the operational features of various privacy-preserving phantom routing protocols and simulates WSNs with varied network configurations to investigate how different routing strategies affect SLP reliability. Safety period and capture ratio metrics are used to compute SLP reliability. Simulation results show that integration of phantom routing with fake packet distribution mechanisms adversely impacts SLP reliability. SLP reliability decreases also as the number of fake packet sources increases. Research proves that a protocol with many fake packet sources achieves SLP reliability for a mission duration of 940 rounds, while a protocol with no fake packet sources achieves SLP reliability for 1658 rounds.
