Relevance. In the modern world, telecommunications play a critically important role in supporting the digital economy. The complexity and scale of contemporary telecommunication networks ‒ characterized by high dynamism, heterogeneity, and continuously growing traffic ‒ necessitate the development and application of efficient optimization methods. Traditional analytical approaches often prove inadequate in addressing the combinatorial complexity and nonlinearity of problems arising in this domain, making the search for alternative solutions increasingly relevant. In this context, swarm intelligence algorithms represent a promising class of methods inspired by the collective behavior of biological organisms, capable of effectively solving complex optimization tasks.

The aim of this study is to systematize and analyze current research devoted to the application of swarm intelligence algorithms in telecommunication networks. Particular attention is given to such methods as the Artificial Bee Colony (ABC) algorithm, Ant Colony Optimization (ACO), and the Grey Wolf Optimizer (GWO), as well as their modifications. The main objective of the research is to identify key trends and development directions of heuristic algorithms aimed at enhancing the performance, reliability, and resilience of telecommunication systems under increasing traffic loads and evolving network architectures.

Scientific novelty lies in conducting a systematic review of recent publications focusing on the practical application of swarm intelligence algorithms in the field of telecommunications. A taxonomy of the considered methods is presented, and their core operational principles and effectiveness in solving specific optimization problems within this domain are analyzed. Special emphasis is placed on the adaptation and hybridization of algorithms to improve their performance in real-world network scenarios.

The theoretical significance of the study consists in summarizing existing practices of applying bio-inspired optimization techniques in telecommunications, thereby opening up opportunities for further development of more efficient and scalable approaches to managing complex dynamic systems. The obtained results contribute to a deeper understanding of the potential of swarm intelligence algorithms in solving routing, resource allocation, network planning, and other critical problems typical of the modern digital economy.

Relevance. Today, universities are equipped with a large number of modern telecommunications and IT equipment, and other material assets. The university needs to manage the life cycle of all material assets. The purpose of the research is to improve the efficiency of university asset management by developing an AMS (Asset Management System) based on an open digital architecture (ODA), which eliminates gaps in the automation of accounting and analysis processes. The growth of data volumes and the need for integration with IT infrastructure underscore the relevance of developing flexible digital solutions.

Research methods: Object-oriented analysis, conceptual modeling, and design based on microservices architecture and ODA standards were applied.

Results. An AMS system was developed, including functional modules for automating equipment write-offs, generating inventory numbers, and analytics. The system integrates via API using ODA-components (TMFC014, TMFC039, etc.), ensuring flexibility and scalability.

For the first time, an approach to decomposing AMS into ODA-components is proposed, enhancing resource management efficiency and process automation. The work demonstrates the dependence of system flexibility on the application of ODA and creates a foundation for further scaling.

The theoretical significance is determined by the development of a system model on the latest ODA framework, and the practical significance is that the proposed result can be practically used to develop specific AMS solutions.

Relevance. The need for intelligent video surveillance is due to the lack of control over the situation at unguarded crossings. For this purpose, it is proposed to use a 4G standard system in the frequency range from 1785 to 1805 MHz allocated to Russian Railways.

Objective: to create a model that allows us to study and simulate the dependence of attenuation on the communication range for intelligent video surveillance systems at unguarded railway crossings.

Methods: using the COST-231 Hata mathematical model based on empirical relationships that take into account the type of terrain, the frequency of the radio signal, the size of objects blocking the route, the distance between them, as well as the heights of base station masts and mobile subscriber antennas.

Results: the obtained refined expression of the model for the conditions under consideration determines the dependence of attenuation on the radio channel route on the distance between the base station and the user equipment of video surveillance platforms based on 4G networks for unguarded crossings located on a section outside urban development. The model takes into account the driver's reaction time and the length of the braking distance for rolling stock of various types.

Practical significance: the results of the work can be used in the design of video surveillance systems on railway transport at unguarded crossings, taking into account the speed of trains on the section under consideration.

Relevance. In statistical analysis of complex envelopes of modulated signals received from a communication channel, the normal distribution density is generally assumed to be the probability density model. However, in a channel with deep fading and in the presence of interference, i.e. in the case of a "complex" signal-interference environment in the channel, distribution models with heavier tails may be of interest. The logistic distribution and the hyperbolic secant distribution are considered as such in the work. Expressions for the corresponding two-dimensional probability distribution densities are presented.

The aim of the work is to show that, under certain conditions, models of the distribution of the complex envelope that other than normal one can be observed in a real communication channel. Taking this into account may allow to improve the characteristics of the communication system in the tasks of adaptation and evaluation of the reliability of demodulator solutions.

Research methods: To check whether the complex envelope belongs to the corresponding distribution law, the Chi-square criterion is used. The implementation of the Chi-square criterion for the case of a two-dimensional distribution density is proposed in article.

As results, the paper presents the analysis of statistical processing of signals received from a real communication channel under various conditions.

The novelty lies in the experimental study of the fact that in real channels, in the case of deep fading and complex signal-interference conditions, the logistic distribution or the hyperbolic secant distribution may be more preferable.

The practical significance lies in the fact that taking into account the distribution model makes it possible to obtain a more adequate estimate of the mean square deviation of the noise component and the signal-to-noise ratio, which is essential for the functioning of adaptive radio communication systems, as well as in the task of evaluating soft demodulation solutions.

Abstract: With the advancement of digital radio communication systems, there is a growing demand for enhanced spectral efficiency in mobile and hybrid radio systems and networks. To meet these requirements, Multiple-Input Multiple-Output (MIMO) technology is extensively employed in modern radio communication systems. The use of multiple transmitting and receiving antennas in MIMO systems imposes stringent performance requirements on signal processing algorithms. Consequently, the development of fast and efficient signal processing algorithms is a task of significant relevance.

The aim of this study is to analyze and optimize space-time coding techniques and signal processing algorithms for MIMO systems. The research focuses on developing an algorithm that ensures the required level of performance while significantly reducing computational complexity.

Methods. This study utilizes numerical simulation methods within the MATLAB environment to compare the performance of various signal processing algorithms in MIMO systems over a fading channel.

Results. In addressing the research objectives, the principles of constructing space-time code matrices for different coding methods were examined, and coherent signal demodulation techniques were analyzed. Based on this analysis, an algorithm with reduced computational complexity is proposed. A key element of scientific novelty of this work lies in the development and application of a novel approach to approximate the inverse channel matrix, which is a computationally expensive operation, particularly for high-dimensional matrices in coherent demodulation algorithms. This new approach is based on the combined use of the iterative Jacobi method and the Neumann series expansion for the approximation of the matrix inverse.

Practical significance. The developed algorithm can be utilized in the design of MIMO systems with a large number of transmitting and receiving antennas, as well as in the application of non-orthogonal coding schemes to increase the coding rate. In such systems, conventional demodulation methods require significant computational resources for inverting the channel matrix, which limits real-world performance. The proposed algorithm mitigates this bottleneck, enabling more practical implementations.

Deep learning methods play a crucial role in enhancing the effectiveness of intrusion detection systems. This study presents a comparative analysis of seven deep learning models, including autoencoders, restricted Boltzmann machines, deep belief networks, convolutional and recurrent neural networks, generative adversarial networks, and deep neural networks. The primary focus is on accuracy, precision, and recall metrics, evaluated using the NSL-KDD dataset. The analysis demonstrated the high effectiveness of recurrent neural networks, which achieved an accuracy of 99.79 %, precision of 99.67 %, and recall of 99.86 %.

The objective of the study: of this paper is to enhance the effectiveness of intrusion detection systems through a comparative analysis of the performance of various deep learning models and an assessment of their applicability in the context of dynamic network security threats.

The proposed solution involves a comparative analysis of seven deep learning models to identify the most effective ones for network security tasks. This analysis aids in selecting the optimal models for specific security requirements.

The evaluation methodology involves the use of the benchmark dataset NSL-KDD, which contains various types of attacks and normal connections. The key evaluation metrics are accuracy, precision, and recall.

The system implementation is based on deep learning frameworks such as TensorFlow. The results of the system’s performance and their interpretation are presented in the paper.

Experiments with the NSL-KDD dataset demonstrated accuracy, precision, and recall for all the deep learning models considered.

The scientific novelty is the ability to obtain formal performance evaluations of various deep learning models for intrusion detection systems, taking into account their architectural features, the processing of temporal and spatial data, as well as the characteristics of network traffic and attack types.

The theoretical significance is the expansion of methods for evaluating the effectiveness of intrusion detection systems through the analysis and comparison of the performance of deep learning models in the context of processing complex and high-dimensional network data.

The practical significance is the application of the comparative analysis results for selecting the most effective solutions in intrusion detection systems and optimizing them for real-world operating conditions.

The relevance. Distributed Denial of Service (DDoS) attacks remain a significant threat to the availability of online services. Traditional intrusion detection systems based on signatures or anomaly analysis face limitations in detecting new and complex attacks, while machine learning-based approaches, while showing high potential, often lack interpretability. Hybrid systems, such as the Adaptive Neuro-Fuzzy Inference System (ANFIS), combine the advantages of neural networks and fuzzy logic, offering both accuracy and interpretability. However, their effectiveness with respect to modern datasets with diverse attack vectors, such as CIC-DDoS-2019, needs to be investigated.

Objective. The study aims to evaluate the performance and applicability of ANFIS for the task of DDoS attack detection using the current and challenging CIC-DDoS-2019 dataset. The ANFIS model was used in this work. The study was conducted on a representative subsample of the CIC-DDoS-2019 dataset. The methodology included careful data preprocessing, selection of the most relevant features and expert knowledge, and feature normalisation. The ANFIS model with Gaussian membership functions was trained using a hybrid optimisation algorithm (gradient descent and least squares method) on 80 % of the data. Performance was evaluated on the remaining 20 % of the test data using standard classification metrics: Accuracy, Precision, Recall, F1-Score, and error matrix analysis.

Results. The experiments showed high performance of the ANFIS model. The following metrics were achieved: proportion of correctly classified objects (Accuracy) ‒ 97.82 %, accuracy (Precision) ‒ 99.52 %, completeness (Recall) ‒ 85.95 % and F1-measure ‒ 92.24 %. The results indicate a very low false positive rate, with some number of missed attacks.

Novelty. The work demonstrates the application and performance evaluation of ANFIS on a modern and complex CIC-DDoS-2019 dataset containing relevant attack types.

The study confirms the theoretical applicability of hybrid neuro-fuzzy models to solve current cybersecurity problems. The practical significance consists in demonstrating that ANFIS can serve as a basis for the development of effective DDoS attack detection systems, providing a high level of accuracy and acceptable detection completeness. The ability to analyze membership functions and rules implements interpretability, which is important for understanding system performance and threat analysis. The results provide benchmarks for ANFIS on this dataset.

Relevance. An integral component of cyberspace are access systems that ensure the distribution of cyberspace information and communication resources among users. The development and implementation of digital technologies requires making adjustments to the architecture and principles of functioning of access systems. At the same time, it should be borne in mind that the requirements imposed on them can be diverse, contradictory and determined by the specifics of the subject area. The purpose of the research is to propose a competitive algorithm for multiple access, the main idea of which is the rejection of the principle of adversarial load sources. The “corporativeness" of the algorithm is manifested in the use of the principle of “fair distribution” of a common resource, the transmission channel, so that all data from all sources of the load is collected and transmitted corporately without delay/loss. The main requirement for the functioning of a corporate multiple access system is to meet the general criterion of optimality. Such criteria can be: the weighted average proportion of data blocks received correctly and on time, or the weighted average delay time in transmitting data blocks, or the weighted average proportion of lost data blocks.

Methods. The article outlines the concept of a corporate multiple access algorithm based on a combined method for dividing a common transmission channel: temporary separation is used between groups of load sources, and random synchronous access is used within each group. To implement the corporate access principle, a dynamic access control procedure is used.

Results. A mathematical model of a corporate multiple access network and expressions for calculating the probabilistic-temporal characteristics of data block transmission have been developed. The optimization problem is formulated: choosing the optimal mode of operation of the access network, which provides for such a distribution of time windows between load sources that the extremum of the general optimality criterion is achieved. A three–stage algorithm for solving the optimization problem is proposed: stage 1 is the calculation of all possible values of the selected optimization criterion, for which the weighted average proportion of data blocks received correctly and on time is taken, stage 2 is the construction of a graphical model of the optimization problem, and stage 3 is the finding of the shortest path for the constructed graph, the set of edges that make up such a path will be solving the problem. The approbation of this algorithm is presented.

The theoretical significance is the expansion in the formalization of the description of the architecture of cyberspace, the development of methods, technologies and mathematical models of multiple access in cyberspace, as well as in the calculated expressions obtained, algorithms for optimizing the functioning of systems that implement a corporate approach to multiple access.

Relevance. Improving collective perception strategies in swarm systems is a key challenge for enhancing the efficiency of autonomous robotic groups in complex and dynamic environments. Existing approaches, such as DMMD, DMVD, and DC, have limited capabilities in classifying objects with non-obvious features, necessitating the development of new methods.

Objective. Increasing the accuracy of perceiving specific characteristics of an object investigated by a multi-agent robotic system.

Methods. The proposed criterion employs a Bayesian decision rule to update the posterior probabilities of alternatives based on data collected by the robots. The validity of the proposed solutions was confirmed through simulation of a typical collective perception task on a defined tested.

Results. A comparison was made with established collective perception strategies: DMMD, DMVD, and DC. It was shown that these strategies have limited applicability in classifying complex objects. A software implementation of the collective perception scenario was tested in a swarm robotic system consisting of 20 robots inspecting a scene composed of multicolored tiles. The experimental results demonstrated that the authors' approach endowed the robot swarm with previously unattainable functional capabilities in collective perception for complex scenarios.

Novelty. A method for detecting object properties using a statistical criterion was proposed. The strategy quantifies the consensus-building process among swarm members over sequential time steps, followed by intra- and inter-period processing of information generated by the swarm's robots. The results expand the theoretical foundations of swarm intelligence by introducing a new method for processing distributed information. Practical significance lies in improving the efficiency of swarm systems for monitoring, search, and classification tasks in medicine, ecology, and other fields.

In modern conditions, the security of higher education institutions requires an integrated approach, including both physical protection and cybersecurity. With the growing number of students, faculty, and visitors, as well as increasing threats such as terrorism, vandalism, and cyberattacks, the implementation of effective access control systems is becoming an urgent task. One of the promising solutions is a system for monitoring temporary passes based on QR codes, which ensures not only the restriction of unauthorized access, but also the collection of data on the movement of persons on the territory of the educational institution. The purpose of this study is to develop a methodology for using QR codes in access control systems of higher education institutions using modern cryptographic algorithms, including symmetric encryption (AES-256), asymmetric elliptic curve cryptography (ECC).

The essence of the proposed solution is an automated system that includes: electronic applications for admission; automatic; QR code generation.codes with encrypted data; control of the validity period of passes; monitoring violations and integration with the Telegram bot for the convenience of users. The principle of the described technique is based on the encryption of the metadata of the pass, followed by the generation of a QR code that can be read and verified by the security service. AES-256 and ECC algorithms are used to ensure a high degree of protection.

The scientific novelty of the solution lies in the combined use of QR codes and modern cryptographic methods, which ensures a high level of security and ease of use in a university setting.

The theoretical significance of the work consists in developing a model of an access control system adapted for educational institutions, taking into account modern threats and requirements of regulatory documents (for example, Decree of the President of the Russian Federation No. 166 dated 30.03.2022).

The practical significance is confirmed by the possibility of direct implementation of the system in educational institutions. The solution allows not only to increase the level of security, but also to optimize administrative processes by automating the issuance of passes and integrating with the Telegram messenger.