Publikační činnost Katedry telekomunikačních technologií / Publications of Department of Telecommunications (440)

Permanent URI for this collectionhttp://hdl.handle.net/10084/64796

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry telekomunikačních technologií (440) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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Now showing 1 - 20 out of 394 results

Federated-reinforcement learning-assisted IoT consumers system for kidney disease images
(IEEE, 2024) Mohammed, Mazin Abed; Lakhan, Abdullah; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Dutta, Ashit Kumar; Memon, Sajida; Marhoon, Haydar Abdulameer; Martinek, Radek
The number of people with kidney disease rises every day for many reasons. Many existing machine-learning-enabled mechanisms for processing kidney disease suffer from long delays and consume much more resources during processing. In this paper, the study shows how federated and reinforcement learning schemes can be used to develop the best delay scheme. The scheme must optimize both the internal and external states of reinforcement learning and the federated learning fog cloud network. This work presents the Adaptive Federated Reinforcement Learning-Enabled System (AFRLS) for Internet of Things (IoT) consumers' kidney disease image processing. The main relationship between IoT consumers and kidney image is that the data is collected from different IoT consumer sources, such as ultrasound and X-rays in healthcare clinics. In healthcare applications, kidney urinary tasks reduce the time it takes to preprocess federated learning datasets for training and testing and run them on different fog and cloud nodes. AFRLS decides the scheduling on other nodes and improves constraints based on the decision tree. Based on the simulation results, AFRLS is a new strategy that reduces the time tasks need to be delayed compared to other machine learning methods used in fog cloud networks. The AFRLS improved the delay among nodes by 55%, the delay among internal states by 40%, and the training and testing delay by 51%.
A Multimodal Perceived Stress Classification Framework Using Wearable Physiological Sensors
(IEEE, 2026) Majid, Muhammad; Arsalan, Aamir; Frnda, Jaroslav; Anwar, Syed Muhammad
Mental stress is a common condition that poses serious health risks, but proper management can greatly improve quality of life. We propose a robust multimodal framework for perceived stress classification using data from forty subjects collected via three physiological modalities: electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). Unlike most existing studies that focus on single modalities and binary classification, our framework addresses both two- and three-class perceived stress problems through multimodal fusion. Data was acquired over three minutes in an open-eye condition, and stress levels were assessed using the Perceived Stress Scale to assign labels. Time-domain features were extracted from GSR and PPG signals, while frequency-domain features were extracted from EEG. A frequency band selection algorithm identified the theta band as optimal for stress classification, and a wrapper-based feature selection method was applied to derive an effective multimodal feature set. Stress classification was performed with three classifiers utilizing features from all modalities. Among these classifiers, a significant accuracy (95% for two classes and 77.5% for three classes) was achieved using multilayer perceptron. The fusion of features from multiple modalities improves perceived stress classification, and our method, based on wearable sensors, is feasible for out-of-lab applications.
Performance analysis of dual-hop mixed RF-FSO systems combined with NOMA
(PLOS, 2024) Hung, Tran Cong; Nguyen, Tan N.; Nhan, N. H. K.; Le, Anh-Tu; Son, Pham Ngoc; Pham, Thu-Ha Thi; Vozňák, Miroslav
This paper investigates the performance of hybrid radio frequency/free space optical (RF/FSO) systems combined with non-orthogonal multiple access communications technology. We examine a scenario where the source and destination are separated by a large distance, with no direct link between them. The relay, denoted R, operates using the decode-and-forward (DF) protocol. Under the DF relaying scheme, the relay employs successive interference cancellation (SIC). In this setup, the FSO link from the source to the relay follows a Gamma-Gamma distribution, while the RF link from the relay to multiple users follow a Nakagami-m distribution. Based on this system model, we analyze the outage probability (OP). Our findings indicate a direct relationship between SIC and OP performance: the higher the SIC capability, the more effective the system. In addition, the system's performance is dependent on the parameters of the FSO channel. Finally, Monte Carlo simulations are presented to further validate our framework and findings.
Visibility control of phase fiber optic sensors in passive optical networks
(Elsevier, 2025) Čubík, Jakub; Kepák, Stanislav; Nedoma, Jan; Fajkus, Marcel; Marques, Carlos
Integrating sensors into existing high-speed data networks delivers an intelligent hybrid network that is able to communicate and deliver a plethora of information about its surroundings. The use of existing fiber optic passive optical networks (PONs) is economically and technically advantageous in the future. Existing networks are often conveniently located, and the measured quantities maybe diagnostics of the network itself but, for example, monitoring of traffic, critical infrastructures, security, construction work, and others. These sensors can have unique features such as long-range interrogation, immunity to electromagnetic interference (EMI), high sensitivity, or, for example, distributed measurements, and their spatial resolution. The techniques of embedding a phase sensor into a PON are discussed in the article, and how the data and sensor part of the network will be affected and the balancing between the two. The measured quantity is the vibration acting on the interferometric fiber optic sensor (IFOS) and the visibility of its phase response (interference). In the case of a data network, it is the stability of the data transmission. Five different methods of sensor insertion have been investigated, simulated, and experimentally tested, showing functional and non-functional ways of integration. For the security of the network data part, the best configurations are those that influence visibility using asymmetric or WDM couplers. Changes in the difference in arm lengths are also a potentially promising method, but the coherent length of the source affects data security. These findings show how sensors can be operated on existing networks but also in what ways data services will be disrupted or completely disrupted.
People detection using artificial intelligence with panchromatic satellite images
(MDPI, 2024) Golej, Peter; Kukuliač, Pavel; Horák, Jiří; Orlíková, Lucie; Partila, Pavol
The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R-CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm.
A novel deep learning framework for intrusion detection systems in wireless network
(MDPI, 2024) Dang, Khoa Dinh Nguyen; Fazio, Peppino; Vozňák, Miroslav
In modern network security setups, Intrusion Detection Systems (IDS) are crucial elements that play a key role in protecting against unauthorized access, malicious actions, and policy breaches. Despite significant progress in IDS technology, two of the most major obstacles remain: how to avoid false alarms due to imbalanced data and accurately forecast the precise type of attacks before they even happen to minimize the damage caused. To deal with two problems in the most optimized way possible, we propose a two-task regression and classification strategy called Hybrid Regression-Classification (HRC), a deep learning-based strategy for developing an intrusion detection system (IDS) that can minimize the false alarm rate and detect and predict potential cyber-attacks before they occur to help the current wireless network in dealing with the attacks more efficiently and precisely. The experimental results show that our HRC strategy accurately predicts the incoming behavior of the IP data traffic in two different datasets. This can help the IDS to detect potential attacks sooner with high accuracy so that they can have enough reaction time to deal with the attack. Furthermore, our proposed strategy can also deal with imbalanced data. Even when the imbalance is large between categories. This will help significantly reduce the false alarm rate of IDS in practice. These strengths combined will benefit the IDS by making it more active in defense and help deal with the intrusion detection problem more effectively.
Distributed sensing with nanoparticle-doped fibers in standard OTDR systems: Validation and performance analysis
(Elsevier, 2026) Leal-Junior, Arnaldo; Kepák, Stanislav; Pedruzzi, Eduarda; Nedoma, Jan; Martinek, Radek; Blanc, Wilfried
This paper presents the implementation of enhanced backscattering optical fibers in optical time-domain reflec tometry (OTDR). The increase in the backscattering is achieved by doping the fiber core with nanoparticles, resulting in the so-called nanoparticle-doped optical fibers (NPFs). The so-called NPF-OTDR sensor system takes advantage of the higher scattering of the NPFs to increase the spatial resolution and strain sensitivity of the distributed sensor system. The wavelength-dependent optical attenuation is measured over a wide range of wave lengths using the cutback method to evaluate the operational wavelength for the OTDR. In this case, the results indicated smaller attenuation in 1310 nm than at the other operational wavelengths of the OTDR system (i.e., 1550 nm and 1625 nm). For this reason, the OTDR is applied at the 1310 nm wavelength for different strains applied along the optical fiber (with 1 m separation between them). The results for strain estimation showed relative errors of around 3%, whereas the strain position estimation is around 0.24 m. These results indicate the feasibility of the proposed system with a potential spatial resolution of around 0.5 m (which can be even lower depending on the OTDR setup). Thus, the advantages of the OTDR using the NPF result in a better sensor system performance when compared with the standard optical fibers, significantly enhancing the conventional OTDR capabilities in terms of resolution with even comparable performance to specialty OTDR systems at a lower cost and with a simpler design.
Symbiotic communication systems in the Internet of Things: A framework for double adaptive performance analysis
(IEEE, 2026) Vu, Thai-Hoc; Nguyen, Tien-Tung; Nguyen, Tan N; Tu, Lam-Thanh; Vozňák, Miroslav
This letter studies the performance enhancement of symbiotic systems, which begins by formulating a closed-form adaptive mutualism symbiotic strategy for the backscatter coefficient to achieve minimal decoding errors for both primary and secondary signals. Then, we analyze two scenarios: First, the primary source adapts the modulation scheme based on the channel conditions of the primary signal to meet the target bit error rate (BER), evaluating metrics: mode selection probability, outage mode probability (OMP), BER, and spectral efficiency. Second, the primary source adapts its transmission rate and/or power according to three channel policies: constant power with optimal rate adaptation, optimal simultaneous power and rate adaptation, and truncated channel inversion with a fixed rate. Results show that for the first scenario, our proposed approach significantly improves the OMP and BER of the adaptive symbiotic system in the moderate and high signal-to-noise ratio (SNR) regimes compared to the fixed one, while the second scenario shows a promising choice for balancing capacity between backscatter and cellular rates in the low SNR regime.
A novel sensing and symbiotic communication approach
(IEEE, 2026) Vu, Thai-Hoc; Le, Anh-Tu; Tu, Lam-Thanh; Luong, Nguyen Cong; Vozňák, Miroslav
As next-generation technologies promise seamless integration of sensing and communication within unified systems by leveraging shared frequencies, signaling, and hardware, it is of interest to explore new communication approaches in a reciprocal and symbiotic manner. This letter proposes a sensing and symbiotic communication (SaSC) approach for uplink scenarios, where the base station simultaneously performs radar sensing, user communication, and backscatter information collection. To enforce symbiotic communications efficiently, adaptive reflective coefficients for backscatter nodes and diversity techniques for user communication receptions are jointly established. Upon this, closed-form expressions are derived to quantify outage probability for symbiotic communication and mutual information for radar sensing. Besides, deep quantitative analyses of high transmit power regimes and low rates are also analyzed to attain insights into system designs and solve a non-convex problem of the radar transmit power optimization to maximize radar mutual information under throughput constraints of symbiotic communication. Moreover, to achieve mutual interference cancellation between radar and symbiotic communication signals, a model of reversed radar symbols over consecutive periods has been outlined as a benchmark scheme. Simulation results affirm the potential and effectiveness of the proposed SaSC approach.
Latent diffusion for spectrum sensing of coexisting radar and communication signals
(IEEE, 2026) Huynh-The, Thien; Huynh, Phuoc-Long; Phan, Van-Ca; Vu, Thai-Hoc; da Costa, Daniel Benevides
The growing demand for spectrum efficiency in next-generation wireless networks, especially in vehicular environments, necessitates effective spectrum sensing (SS) techniques capable of managing the coexistence of technologies like fifth generation new radio (NR) and radar systems. This letter introduces SpecDiff, an innovative framework based on latent diffusion models for spectrogram segmentation, designed to identify and differentiate these coexisting signals in dynamic, noisy environments. SpecDiff leverages a generative diffusion model in a compact latent space, using an attention-based denoising process to enhance segmentation performance under low signal-to-noise ratios and complex channel conditions. The model achieves state-of-the-art performance, with a mean accuracy of 98.68% and mean intersection-over-union (IoU) of 96.30%, effectively identifying the occupied bandwidth in spectrograms. Furthermore, SpecDiff surpasses existing deep learning models in both accuracy and efficiency, offering a promising solution for spectrum sharing in future wireless networks.
Enhancing transparency and efficiency in blockchain harvest: Empowering farmers and consumers through transparent trading in agricultural applications
(Elsevier, 2025) Lakhan, Abdullah; Mohammed, Mazin Abed; Al-Budair, Lilian Qasim Alwan; Memon, Sajida; Slaný, Vlastimil; Deveci, Muhammet; Martinek, Radek
With the development of edge-cutting technologies, digital agriculture farming, product selling, and purchasing have been increasing progressively. On the other hand, transparency in digital agricultural agrochemicals (pesticides or herbicides) substances of products needs to be monitored carefully between production and selling to customers in a transparent form. Recently, blockchain has emerged as a decentralized technology, which is the most potent decentralized technology. It connects many nodes and validates their data transparency during application sharing. Therefore, it is a motivation to use blockchain technology for digital agriculture applications to meet data transparency, security, and privacy requirements. In this paper, we present enhancing transparency and efficiency in blockchain harvest: empowering farmers and consumers through transparent trading in agricultural application tasks. The application tasks are a mixture of agricultural things (IoT) sensors, products, monitoring, fertilizers, transport tracking, farmers, consumers, and institutional data for processing in digital healthcare applications. Our objective is to process the agricultural tasks during production and trading in an immutable, transparent, secure, and private form. To meet constraints such as processing time, blockchain validation, access control, and cyber-attacks, we suggest enhancing transparency and efficiency in blockchain harvesting and empowering farmers and consumers to trade securely and transparently. In proposed blockchain agriculture, all nodes are heterogeneous and connected; therefore, to avoid any time failure, cyberattacks, or block failure, we establish them in a stable form. This paper presents the agriculture blockchain harvest multi-tasking scheduling (ABHMTS) algorithm methodology, which amalgamates different methods: smart contracts, local offloading, multi-tasking, and blockchain validation with malware detection in blocks during data transfer among other nodes. The experimental results in simulation show that ABHMTS improved the transparency of agricultural task data by 98% and executed them with the minimum processing time by 20%, and reduced the risk of resource failure and resource consumption by 10% with higher malware detection in the framework as compared to baseline approaches.
POF-based biosensors for cortisol detection in seawater as a tool for aquaculture systems
(Springer Nature, 2024) Arcadio, Francesco; Soares, Simone; Nedoma, Jan; Aguiar, Dayana; Pereira, Ana Cristina; Zeni, Luigi; Cennamo, Nunzio; Marques, Carlos
A surface plasmon resonance (SPR) phenomenon implemented via D-shaped polymer optical fiber (POF) is exploited to realize cortisol biosensors. In this work, two immonosensors are designed and developed for the qualitative as well as quantitative measurement of cortisol in artificial and real samples. The performances of the POF-based biosensors in cortisol recognition are achieved using different functionalization protocols to make the same antibody receptor layer over the SPR surface via cysteamine and lipoic acid, achieving a limit of detection (LOD) of 0.8 pg/mL and 0.2 pg/mL, respectively. More specifically, the use of cysteamine or lipoic acid changes the distance between the receptor layer and the SPR surface, improving the sensitivity at low concentrations of about one order of magnitude in the configuration based on lipoic acid. The LODs of both cortisol biosensors are achieved well competitively with other sensor systems but without the need for amplification or sample treatments. In order to obtain the selectivity tests, cholesterol and testosterone were used as interfering substances. Moreover, tests in simulated seawater were performed for the same cortisol concentration range achieved in buffer solution to assess the immunosensor response to the complex matrix. Finally, the developed cortisol biosensor was used in a real seawater sample to estimate the cortisol concentration value. The gold standard method has confirmed the estimated cortisol concentration value in real seawater samples. Liquid-liquid extraction was implemented to maximize the response of cortisol in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) analysis.
The role of photonic sensing technologies in Healthcare 5.0: A comprehensive review of future perspectives and applications
(IEEE, 2025) Leal-Junior, Arnaldo; Nedoma, Jan; Martinek, Radek
This article presents a review of Healthcare 5.0 and its historical perspective, considering the industrial revolutions. The main aspects and challenges of the next generation of healthcare systems are discussed. Due to the inherent advantages of optical fiber sensor (OFS) systems, such technology is comprehensively reviewed and discussed in terms of healthcare applications. Thereafter, an analytical correlation is performed between OFS technologies and the requirements of Healthcare 5.0, and discussed in detail. The key parameters in Healthcare 5.0 indicated that OFS technology aligns well with the requirements and challenges of Healthcare 5.0. Thus, photonic technologies play a key role in the future of healthcare, especially in innovative approaches for integrated sensing and communication (ISAC), as such approaches are readily available for use with optical fibers that combine excellent signal transmission and distributed sensing capabilities. Thus, the contributions of this work are related to the first review of Healthcare 5.0, focused on the role of photonic technologies in such new healthcare evolution, as well as the analysis concerning the key aspects of such applications.
Advanced fiber optic sensors for quantitative nitrite detection: Comparative analysis of plasmonic tilted fiber Bragg gratings and fiber optic tips with ion-imprinted polymers
(Elsevier, 2024) Liu, Xuecheng; Singh, Ragini; Zhang, Bingyuan; Caucheteur, Christophe; Santos, Nuno; Kumar, Santosh; Nedoma, Jan; Marques, Carlos
The presence of nitrite, a prevalent contaminant in natural environments, presents a significant environmental and human health concern. Hence, it is imperative to develop a sensor with the ability to quantitatively detect nitrite. This study focuses on the design and development of i) probe 1: tilted fiber Bragg gratings (TFBGs) and ii) probe 2: fiber optic tip-based plasmonic sensors utilizing ion-imprinted polymers. The concentration of nitrite was assessed at various levels using both sensing configurations. The outcomes indicated that the TFBGs-based sensor exhibited a sensitivity and limit of detection (LOD) of 0.469 nm/ln(mu g/mL) and 0.142 mu g/mL in the linear detection range of 0.5-50 mu g/mL. The fiber optic tip-based sensor exhibited a sensitivity and LOD of 1.16 nm/ln (mu g/mL) and 0.176 mu g/mL within the 1-50 mu g/mL linear detection range. The obtained sensing results reveal that the sensors presented in this study are able to accurately detect nitrite at various concentrations in a quantitative manner. Moreover, an assessment was conducted to examine the selectivity and reusability of the sensor individually, yielding satisfactory results.
Statistical analysis of the sum of double random variables for security applications in RIS-assisted NOMA networks with a direct link
(MDPI, 2025) Nguyen, Sang-Quang; Tran, Phuong T.; Minh, Bui Vu; Duy, Tran Trung; Le, Anh-Tu; Rejfek, Luboš; Tu, Lam-Thanh
Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) to perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided wireless networks, the statistics of a single random variable plus the sum of double random variables becomes a core approach to reflect how communication links from RISs improve wireless-based systems versus direct ones. With this in mind, the work applies the statistics of a single random variable plus the sum of double random variables in the secure performance of RIS-based non-orthogonal multi-access (NOMA) systems with the presence of untrusted users. We propose a new communication strategy by jointly considering NOMA encoding and RIS's phase shift design to enhance the communication of legitimate nodes while degrading the channel capacity of untrusted elements but with sufficient power resources for signal recovery. Following that, we analyze and derive the closed-form expressions of the secrecy effective capacity (SEC) and secrecy outage probability (SOP). All analyses are supported by extensive Monte Carlo simulation outcomes, which facilitate an understanding of system communication behavior, such as the transmit signal-to-noise ratio, the number of RIS elements, the power allocation coefficients, the target data rate of the communication channels, and secure data rate. Finally, the results demonstrate that our proposed communication can be improved significantly with an increase in the number of RIS elements, irrespective of the presence of untrusted proximate or distant users.
A hierarchical set-enumeration tree enabling high occupancy item set mining and the use of an adaptive occupancy threshold
(Springer Nature, 2025) Tran, Thanh-Nam; Hoang, Vinh Truong; Truong, Thanh-Cong; Vozňák, Miroslav
The highly efficient HEP algorithm is a useful tool for mining High Occupancy (HO) item sets. Occupancy is an important measure that describes the interestingness of frequent item sets. The current study examines the efficiency problems in mining HO item sets and proposes an improved HEP algorithm, named advanced HEP (A-HEP), based on set theory rules which eliminate a large number of redundant iterations. The study also proposes a novel adaptive-and-modified HEP (NAM-HEP) algorithm that uses HO Set-Enumeration (SE) trees to store HO item sets. The study proposes definitions for adaptive thresholds such as support threshold and occupancy threshold based on the attributes of the transaction database for efficient pruning of the HO-SE tree. Two pseudo-code blocks are presented in addition to a detailed description of the A-HEP and NAM-HEP algorithms and their advantages. Using the A-HEP and NAM-HEP algorithms, HO item sets are investigated from the practical transaction databases named mushroom and retail. The results indicate that the proposed A-HEP and NAM-HEP algorithms enhance mining performance and runtime benchmarks.
Emulation of quantum key distribution networks
(IEEE, 2025) Mehić, Miralem; Dervišević, Emir; Burdiak, Patrik; Lipovac, Vlatko; Fazio, Peppino; Vozňák, Miroslav
Network emulators play an important role in testing network systems, applications, and protocols. Emulators bridge the gap between simulation setups that lack realism in results and real-world trials that are accurate but often expensive, non-reproducible, and uncontrollable. This article presents an extended model of the Quantum Key Distribution Network Simulation Module (QKDNetSim) with a model catalog of QKD components and functionalities. We explore emulations of point-to-point connections in QKD networks and the interaction of essential components within QKD nodes. The presented tool will undoubtedly spur future development and teaching, and it is critical for testing novel applications and protocols applied to QKD networks.
On the effect of coverage range extent on next-cell prediction error for vehicular mobility in 5G/6G networks: a novel theoretic model
(IEEE, 2025) Fazio, Peppino
Over the last decade, 5G and forthcoming 6G archi tectures have undergone extensive standardization and prepara tions for the future. The literature in this field is saturated with studies on predicting mobile trajectories in cellular systems and guaranteeing quality of service and an adequate user experience in these environments. The current study aims to bridge mobility prediction and 5G/6Gpredictive approaches and demonstrate that the intrinsic paradigm of femto-cell and nano-cell deployment (based on very small radio coverage radii) for 5G provides the means to obtain more accurate time series data on user mobility and thus enable predictive models (e.g., machine learning) as suit able technologies for integration with 6G standards. This field is therefore an important avenue of research.
Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images
(Elsevier, 2024) Fazio, Peppino; Mehić, Miralem; Vozňák, Miroslav
Mobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated.
Emerging technologies for 6G non-terrestrial-networks: From academia to industrial applications
(IEEE, 2024) Nguyen, Cong T.; Saputra, Yuris Mulya; Huynh, Nguyen Van; Nguyen, Tan N.; Hoang, Dinh Thai; Nguyen, Diep N.; Pham, Van-Quan; Vozňák, Miroslav; Chatzinotas, Symeon; Tran, Dinh-Hieu
Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era.

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