The problem of the Modulation and Coding Scheme (MCS) selection in 5G V2X systems with Massive MIMO base stations is investigated. The high user mobility results in rapid aging of channel state information, which degrades the performance of conventional MCS and precoder selection algorithms. An MCS and precoder selection algorithm is proposed that is based on the autoregressive model for channel prediction with adaptive triggering of model retraining. Simulations show that the proposed algorithm can significantly reduce the channel resource consumption as compared with the algorithms that use a fixed model update period, while ensuring the desired reliability and data delivery time in variable user speed scenarios.
The multiuser multiple-input multiple-output (MU-MIMO) mechanism is a key component of modern Wi–Fi networks, enabling substantial performance gains in dense multiuser scenarios. Current generation Wi-Fi relies on explicit channel sounding, which involves a lengthy transmission of channel state information. To reduce this overhead, implicit sounding is considered. This work develops a Wi-Fi prototype based on software-defined radio that supports explicit sounding and two implicit sounding methods: one with sequential and one with simultaneous (multiplexed) pilot transmission from the stations. The latter shortens the sounding procedure, but our experiments show that it reduces the signal-to-interference-plus-noise ratio of the channel estimate by up to 4 dB compared with the sequential method. Due to the degradation in implicit channel-sounding quality, reciprocity-calibration errors arise, and the throughput of MU-MIMO transmissions decreases by 20–25
The paper presents a nonlinear filter for state estimation of a process with correlated highest-order derivative from the observations corrupted by the Gaussian white noise of unknown intensity. The filtering scheme is based on nonlinear transformation of the innovation process to approximate the Kalman–Bucy gain matrix, given arbitrary noise intensity. The filter is defined by the differential system of the same dimension as the state vector avoiding equations on the error covariation matrix. Based on the results of computer simulations, recommendations are given for selecting the tuning parameter of the nonlinear filter to take into account the correlation in the signal model.
To meet the increasing demands of wireless networks, the IEEE 802.11be standard introduces support for Multi-Link Operation (MLO) that enables multi-link devices (MLDs) to transmit and receive data simultaneously over several channels. To track the delivery status of multiple packets, MLDs employ a unified sliding window shared across all links. Due to its finite size, the numbers of packets transmitted by an MLD concurrently over different links are interdependent. Under lossy conditions, the number of packets available for transmission within the sliding window additionally reduces, leading to smaller transmission sizes and throughput degradation. We propose the first asynchronous algorithm that selects transmission size of MLDs with an arbitrary number of the links. This algorithm accounts for channel losses and the finite sliding window size. Through simulation, the performance of the proposed algorithm is evaluated across a wide range of scenarios. It is shown that the proposed algorithm increases throughput by up to 12
This study introduces an integrated control framework that combines stochastic Petri nets with ontology-based knowledge representation to improve safety and efficiency during collaborative human–robot assembly. The objective is to endow a robotic coworker with the ability to reason about uncertain human actions while respecting semantic task constraints. The method maps ontology concepts, relations, and individuals to places, transitions, and tokens of a stochastic Petri net, yielding a unified state-space model governed by probabilistic firing rates and logical axioms. Procedures include formal reachability analysis for deadlock and hazard detection, synthesis of an optimal task-allocation policy that maximizes a cumulative reward balancing speed and safety, and implementation of the entire model in the Webots simulator. Experimental investigations were carried out on a pick-and-place assembly scenario involving a six-degree-of-freedom manipulator and a human operator across 100 simulation trials. The proposed controller achieved a 27
The paper presents a new method for visual support of the transcatheter aortic valve replacement (TAVR) procedure, based on a multi-level approach to detecting key points on X-ray images. In contrast to traditional approaches, the authors propose using a group structure of landmarks (anatomical, instrumental, and contour), which allows for taking into account the relationship and joint appearance of points during the operation. For implementation, a multi-task neural network architecture with a single feature extractor and specialized outputs performing visibility classification and regression of point coordinates for each group is proposed. During the experiments, various base models (ResNet18, ResNet34, ResNet50, EfficientNet B7, and VGG16) were compared, among which the ResNet34-based model demonstrated the best performance, achieving a classification accuracy (precision) of 92.35
Satellite broadband access networks require arrangement of receiving communication and data services by subscribers using a service channel through which technological information is exchanged between subscribers and the central Earth station in order to organize subscriber sessions. Analysis of possible procedures of data exchange over the service channel, taking into account similar solutions for terrestrial mobile networks and the specifics of satellite networks, shows that these procedures are close in logic to the S-ALOHA protocol, with the difference that the stream of slots for random multiple access is thinned out due to the need to reserve some slots for transmitting confirmations of the parameters of dedicated traffic channels. A criterion for choosing the preferred procedure is ensuring its successful operation with a specified high probability in the shortest possible time. The aim of this study is to develop a model that makes it possible, depending on the service channel operation procedure used, to estimate the time required to achieve a specified probability of successful operation with an accuracy acceptable for practice. The model is a tool for comparing the procedures of the service channel and selecting preferred options, providing a selective time allocation function for processing the procedure for exchanging technological information, based on which estimates of the required time are given to achieve a specified probability of successful operation. Three options of the procedures that use different ways of conflict resolution are implemented. In addition, the model allows for the inclusion of program blocks describing other procedure options and can be used to justify decisions on designing satellite broadband access networks.
This paper presents an approach for identifying neurophysiological markers of fatigue based on the analysis of EEG sensorimotor rhythms using deep learning methods. A methodology is proposed for generating spectrograms from multichannel EEG signals, including normalization, preprocessing, and data segmentation. Three adapted convolutional neural network architectures (EEGSpecResNet2D, EEGSpecMobileNetV3, EEGSpecAlexNet) are compared in both multiclass and binary classification tasks related to fatigue phases. EEGSpecResNet2D demonstrated the highest accuracy and robustness against overfitting. Additionally, training dynamics were analyzed, and pattern recognition features during intermediate task phases were visualized. The results confirm the effectiveness of residual architectures and anisotropic signal processing for monitoring cognitive load. The study highlights the potential of neural network models for developing BCI interfaces aimed at assessing and predicting operator functional states.
Holographic MIMO (H-MIMO) surfaces are planar structures composed of multiple active or passive unit cells capable of changing their configuration to form a wave with specified parameters. In H‑MIMO surfaces, the spacing between unit cells is less than half the wavelength, which, on the one hand, enables the formation of highly directive beams, but on the other hand, leads to the need to take into account the mutual coupling between cells. This study presents a comparative analysis of the accuracy of a dipole-based analytical model for computing the mutual coupling matrix in H-MIMO surfaces. The discrepancy between the mutual coupling matrices derived through the CST Microwave Studio numerical simulation and those calculated using an analytical model that relies on pairwise interaction calculation for dipole-like unit cells is investigated. The numerical results obtained show that, as the spacing between H-MIMO surface elements decreases below half the wavelength, the fidelity of the analytical model degrades substantially due to the pronounced mutual coupling. It is shown that employing this approximate analytical matrix for the H‑MIMO surface configuration leads to a significant channel gain degradation as compared with the configuration based on the CST-simulated coupling matrix.
The problem of suppressing and modeling metal artifacts in X-ray computed tomography has been recognized since the advent of the computed tomography technique and still lacks a universal solution. We investigate the role of backscattering in a genesis of metal artifacts in computed tomography. It is demonstrated that in the presence of strongly absorbing inclusions leading to complete absorption, accounting for backscattering enables the generation of realistic projection data and metal artifacts in the reconstructed images. The results refine the model of metal artifact formation, which is critically important for the development of artifact correction methods of this type.
Currently, virtual reality (VR) devices are becoming more and more popular. For user convenience, these devices should be portable and able to exchange large amounts of data quickly and reliably through a wireless communication channel. To meet these requirements for the quality of service for VR traffic, Wi-Fi 7 technology proposes a new mechanism called Restricted Target Wake Time (R-TWT). Using simulation, this paper investigates different VR traffic delivery methods in the Wi-Fi network using the R‑TWT mechanism in combination with the basic random access mechanism.
In recent years, the development of network security protocols has focused on enhancing user privacy. A key innovation is the Transport Layer Security Encrypted ClientHello (TLS ECH) amendment. It hides vulnerable service parameters that are currently transmitted in plain text and reveals the requested service. However, traffic metadata, such as packet lengths and inter-arrival times, remain a source of privacy leaks. Existing methods for addressing these vulnerabilities typically do not modify the connection establishment process but rather focus on modifying application-level traffic. However, traffic patterns during connection establishment still reveal the service to which the client is connecting. Therefore, this study develops a new method of TLS ECH connection metadata obfuscation to enhance user privacy. The proposed solution reduces the effectiveness of modern classifiers hRFTC, hC4.5, and UW from 97–99 to 15–31
The deployment of autonomous vehicles is a key driver in the development of intelligent transportation systems. One of the most relevant cases of utilization of autonomous vehicles is the vehicle platooning aimed at improving road safety and road infrastructure utilization. To coordinate platoon members using wireless communication, the 3GPP consortium develops the 5G Vehicle-to-Everything (V2X) technology. A key challenge in 5G V2X networks is the selection of transmission parameters, including the Modulation and Coding Scheme (MCS), to meet the strict latency and reliability requirements. This study proposes an adaptive MCS selection algorithm based on estimating the packet loss probability for different available MCSs. It is shown using the ns-3 simulation that the adaptive algorithm provides the network capacity close to the upper bound achievable by an exhaustive search over available MCSs. Furthermore, the proposed adaptive algorithm increases the network capacity by up to 180
Frequency dependences of the linear dichroism coefficient (LDC) of linearly polarized TEM-waves of p- and s-polarization incident on a metasurface made of graphene nanoribbons (GNRs) are calculated for different values of the Fermi level of graphene in the THz range. It is shown that the GNR metasurfaces exhibit polarization-selective absorption of orthogonally polarized TEM-waves, while the operating frequency and LDC maximum are dynamically controlled by changing the Fermi level.
An object attitude control system suggests installing a four-input navigation receiver and a rigid frame with four antennas corresponding to each receiver input on an object. As navigation receiver measurements, high-precision, but ambiguous carrier-phase pseudoranges are used. A method is proposed for resolving integer ambiguities in the first and second carrier-phase pseudorange differences using rough a priori data on the roll, pitch, and heading (Euler angles) from inclinometers and a heading meter with a high confidence level. This method significantly reduces the initialization time, i.e., the time required to ensure the more efficient operation of the object control system as compared with the methods used previously. The simulation made has demonstrated the high efficiency of the proposed method for resolving phase ambiguities. The precision of the Euler angle estimation by the former using the second carrier-phase pseudorange differences for eight observed satellites is 0.003 rad, compared to 0.001 rad when using the first carrier-phase pseudorange differences. The proposed method ensures an attitude control system initialization time of one reading for the first carrier-phase pseudorange differences and of at least three readings for the second carrier-phase pseudorange differences. Minimization of the initialization time is crucial for dynamic objects, which indicates the priority use of the first carrier-phase pseudorange differences during the processing. Furthermore, the use of the second carrier-phase pseudorange differences leads to significant anomalous errors in the phase ambiguity resolution, which is not observed when working with the first carrier-phase pseudorange differences. However, using the first carrier-phase pseudorange differences requires careful calibration of the attitude control system’s antenna feeder path, which is quite challenging. The presented material can be used by specialists in satellite navigation, geodesy, and related disciplines.
The object of the study is the architecture of generative adversarial networks (GANs) for traffic classification. The subject of the study is the methodology for analyzing the classification accuracy of GAN models. The article develops and tests a methodology for evaluating the effectiveness of GAN architecture, as well as methods for optimizing such models. Since the GAN discriminator is trained not only on real data, but also on synthetic data, this allows it to “predict” future changes in the analyzed data. Therefore, the results of the study can be applied primarily in the development of Deep Packet Inspection (DPI) and Intrusion Detection System and Intrusion Prevention System (IDS/IPS) modules for analyzing network protocols and services, as well as in other areas where input data can often change its parameters.
Polar coding has been adopted in the fifth generation (5G) wireless communication standard, primarily for control channel encoding and decoding in the ultra-reliable and low latency communication (URLLC), and massive machine-type Communication (mMTC) scenario due to its strong error-correcting capability and efficiency. This paper offers a comprehensive examination of polar codes, emphasising their design principles and particular functions in the context of 5G. Fundamental ideas are examined, including channel polarisation and the benefits of polar codes over more conventional methods like Turbo and low-density parity-check (LDPC) codes. It is detailed how polar codes are included in the 5G standard, particularly for the Physical Downlink Control Channel (PDCCH) and Physical Uplink Control Channel (PUCCH), and how they improve data speed, latency, and reliability. Notwithstanding these advantages, several research gaps still exist, such as the requirement for better decoding algorithms to lower complexity and latency, difficulties with effective hardware implementation, and the requirement for adaptable code building to change with the channel. This paper also looks at current initiatives to fine-tune polar codes for real-time deployment and integration with cutting-edge 5G technologies like network slicing and enormous multiple-input multiple-output (MIMO). To sum up, this paper highlights how artificial intelligence techniques can improve the functionality and adaptability of polar codes. It also identifies areas for future research, including improved coding techniques, low-power hardware design, and expanding the applicability of polar codes to next-generation networks beyond 5G.
Methods for forming the generator matrix of a linear network code are considered to determine a method that allows it to be formed without singular submatrices in the Galois field and ensures minimal redundancy to eliminate lost data (packets). Simulation results are presented confirming the feasibility of using matrices based on Singleton configurations as generator matrices for a linear network code, minimizing its redundancy and ensuring the absence of singular submatrices in the Galois field.
The average delay of information exchange in the downlink communication of a low Earth orbit satellite network (LEO SN) between a repeater satellite (RS) with an active phased antenna array (APAA) with discretely scanning beams and ground subscribers has been studied. To calculate the average information transmission delay time in the downlink communication, a mathematical model in the form of a queuing system has been developed. Analytical expressions have been obtained that relate the average delay time to the main network parameters: the channel capacity, number of scanning beams, number of time slots in a scanning frame, etc. for two beam scanning algorithms: static and dynamic. The dependences illustrating the advantages of the discretely scanning beam technology are presented. Estimates of the APAA size depending on a number of beams, radiation pattern width, and scanning sector size are given.
The article is devoted to computer simulation modeling of nonlinear processes in social communications with threshold and impulse effects when launching the activity of opposing communities. Popular modern means of online communication in the era of information society (messengers, the blogosphere, and social networking sites) are used to purposefully form a distorted information matrix of behavior. Not only is hidden advertising being promoted on blogs, but also campaigns are being conducted to disseminate informational outrage and content to shape destructive ideological narratives online. The coordinated deployment of fake news to influence behavior has been clearly demonstrated by the noisy campaign of psychological attacks against vaccines. During the coronavirus pandemic, anti-vaccination propaganda has rapidly intensified online. The injection of prepared messages about the terrible harm of vaccines spread across social networks in pulsed waves. Processes in online communities have become analogous to the pulsating activity of the coronavirus itself. Suddenly, these information waves would spread beyond the local online communities that generated them, once a certain critical mass of active participants in the dissemination of fake news had been reached. Extreme information processes are sharply activated after reaching a certain critical level of activity, but then fade away after the interest of the main audience decreases and the trend loses its relevance. The destructive influence of the advancement of waves of false and psychologically aggressive information on the behavior of the active part of society persists for years. To model the phenomena of social network disturbances, it is necessary to consider the role of critical states and threshold effects in the build-up of excitement on the network. It is relevant to consider models of critical explosive scenarios for the emergence of so-called “hype waves.” Unfortunately, due to psychology, people join a movement en masse if they see many of its participants around them, but this large number is easily created by artificial manipulation. Information noise is always present, but sometimes the influx of content into the network turns into an avalanche, causing secondary waves, but, like an epidemic, an avalanche of rapid spread can die down. For the purposes of predicting the spontaneous launch of hype waves, we propose modifying the equations of models with active critical equilibrium points. Levels and special states change the course of development of the situation in the information space and describe the transformation of content into viral information. Two methods for modeling the impact of critical states for different information environments are considered. Continuous equations have been modified based on the idea of including a threshold unstable value on the right side as a critically permissible saturation of the community with active trigger content, which immediately causes the formation of a wave in the information space. Modifications with a single threshold value in the delay equation for generating sharply damped oscillations are considered. Hybrid computational structures are proposed to describe the flexible adaptive transformation of the threshold level of some information saturation, which abruptly leads to an outbreak of the spread of injected information in the network community. The hybrid continuous-event model forms several unstable stationary states in the iteration dynamics, during the transitions between which the system behavior changes sharply. The new method allows us to describe crisis development scenarios in Withnetwork systems even with weak disturbance of the information space. The resulting models exhibit diverse behavior with the occurrence of bifurcations, the emergence of cycles and alternative attractors. The model requires an extension of behavior and a search for solutions with different properties to describe waves in network structures.
