Communication Network Management Research Articles

Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.

Особливості організаційно-правового забезпечення інформаційної безпеки в умовах війни з росією

The article, based on a theoretical and practical study of the essence and features of the regulation of information and legal relations in the conditions of russian aggression, focuses on the problematic issues of organizational and legal provision of information security as an activity aimed at the prevention, timely detection and termination of threats that destructively affect vital interests of the individual, society and the state in the information sphere. It has been proved that the analysis of the results of the adoption of international legal acts by the UN General Assembly in recent years, with their short-sighted and futile vision, confidently prove that the entire burden of the organizational and legal provision of information security in the formation of principled positions on the regulation of norms aimed at the development of a single mechanism in counteracting threats to geopolitical information security still rests with states within their territorial jurisdiction. It has been determined that the informational pressure, which russia has continuously been using against Ukraine over the past decade, is being accompanied by frantic information propaganda aimed at discrediting public authorities, baseless accusations of the Armed Forces of Ukraine for the deaths of people in the temporarily occupied territory, and the imposition of the chauvinistic idea of defending “russian” peace”, the return of the post-Soviet republics to the “renewed Union” with its imperial goals of aggression. Taking into account the problems of organizational and legal provision of information security, an analysis of the implementation of legal acts regulating informational and legal relations in the conditions of the russian-Ukrainian war has been carried out. Attention has been drawn to the order of creation and running of the system of operational and technical management of electronic communication networks of public use and the national centre of management of electronic communication networks for the purposes of defence and security of the state in conditions of martial law. The issues of combating phishing as a form of information attack, which, having been presented as a simple electronic message, is carried out by criminal groups in a deceptive way on the user in order to obtain any type of data, have been studied out. The problems associated with the implementation of the filtering system for phishing domains, which may create additional significant risks and pose threats to the information security of Ukraine, have been outlined. The mechanisms of organizational and legal protection against disinformation and the means of countering it have been defined. Taking into account the recommendations of the numerous scientists and practitioners, proposals have been made to optimize the organizational and legal provision of information security in the conditions of russian aggression.

Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks

Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.

Innovative Architectures and Management Strategies in 5G Communication Networks

The evolution of communication networks has witnessed a remarkable transformation with the advent of 5G technology. As the latest generation of cellular networks, 5G promises to revolutionize the way we connect, communicate, and interact with the digital world. This paper explores the architecture, management, and implications of 5G communication networks, shedding light on the key features, challenges, and opportunities presented by this transformative technology. At the core of 5G networks lies a sophisticated architecture designed to deliver unprecedented levels of speed, reliability, and connectivity. The 5G New Radio (NR) architecture encompasses three primary components: the User Equipment (UE), the Radio Access Network (RAN), and the Core Network (CN). These components work in tandem to facilitate seamless communication between user devices and the broader network infrastructure. One of the defining features of 5G networks is their ability to support significantly higher data speeds and lower latency compared to previous generations. With the potential to deliver data rates of several gigabits per second and latency as low as a few milliseconds, 5G opens the door to a plethora of innovative applications and services, ranging from immersive virtual reality experiences to real-time remote surgeries. Effective management of 5G networks is paramount to ensuring optimal performance, reliability, and security. Network management systems employ advanced algorithms for dynamic resource allocation, quality of service (QoS) optimization, and network slicing. Network slicing, in particular, enables operators to create virtual networks tailored to specific use cases, such as enhanced mobile broadband, massive IoT deployments, and mission-critical communications. However, the deployment and management of 5G networks pose several challenges and considerations. Infrastructure deployment requires substantial investment and coordination, while ensuring interoperability between different vendors' equipment and standards remains a pressing issue. Spectrum availability and allocation also play a critical role in determining the performance and capacity of 5G networks, necessitating careful regulatory oversight and spectrum management strategies. Despite these challenges, the implications of 5G technology are far-reaching and profound. From empowering smart cities and autonomous vehicles to enabling the widespread adoption of IoT devices and applications, 5G networks have the potential to reshape industries, drive innovation, and enhance the quality of life for billions of people around the world. 5G communication networks represent a paradigm shift in the way we connect and communicate. With their advanced architecture, dynamic management capabilities, and transformative potential, 5G networks are poised to unlock new opportunities, accelerate digital transformation, and usher in a new era of connectivity and innovation.

Machine learning-inspired intelligent optimization for smart radio resource management in satellite communication networks to improve quality of service

Satellite communication networks are seeing a significant surge in traffic requirements. Nevertheless, the rise in traffic requirements is inconsistent across the service region because of the unequal distribution of consumers and fluctuations in traffic requirements during the day. Variable payload designs solve this issue by enabling the uneven allocation of payload resources to match the traffic requirement of each beam. Optimization-based Radio Resource Management (ORRM) has its high substantial efficiency demonstrated computational difficulty hinders its real-world deployment. This work explores the structure, execution, and uses of Machine Learning (ML) for resource allocation in satellite systems. The primary emphasis is on two systems: one that offers power, capacity, and beamwidth adaptability and provides temporal flexibility via beam hopping. The research examines and contrasts several ML methods suggested for these structures. The research determines whether training must be done online or offline depending on the features and needs of each ML method. The study analyzes the most suitable system structure and the pros and cons of each strategy.

Innovative Architectures and Management Strategies in 5G Communication Networks

The evolution of communication networks has witnessed a remarkable transformation with the advent of 5G technology. As the latest generation of cellular networks, 5G promises to revolutionize the way we connect, communicate, and interact with the digital world. This paper explores the architecture, management, and implications of 5G communication networks, shedding light on the key features, challenges, and opportunities presented by this transformative technology. At the core of 5G networks lies a sophisticated architecture designed to deliver unprecedented levels of speed, reliability, and connectivity. The 5G New Radio (NR) architecture encompasses three primary components: the User Equipment (UE), the Radio Access Network (RAN), and the Core Network (CN). These components work in tandem to facilitate seamless communication between user devices and the broader network infrastructure. One of the defining features of 5G networks is their ability to support significantly higher data speeds and lower latency compared to previous generations. With the potential to deliver data rates of several gigabits per second and latency as low as a few milliseconds, 5G opens the door to a plethora of innovative applications and services, ranging from immersive virtual reality experiences to real-time remote surgeries. Effective management of 5G networks is paramount to ensuring optimal performance, reliability, and security. Network management systems employ advanced algorithms for dynamic resource allocation, quality of service (QoS) optimization, and network slicing. Network slicing, in particular, enables operators to create virtual networks tailored to specific use cases, such as enhanced mobile broadband, massive IoT deployments, and mission-critical communications. However, the deployment and management of 5G networks pose several challenges and considerations. Infrastructure deployment requires substantial investment and coordination, while ensuring interoperability between different vendors' equipment and standards remains a pressing issue. Spectrum availability and allocation also play a critical role in determining the performance and capacity of 5G networks, necessitating careful regulatory oversight and spectrum management strategies. Despite these challenges, the implications of 5G technology are far-reaching and profound. From empowering smart cities and autonomous vehicles to enabling the widespread adoption of IoT devices and applications, 5G networks have the potential to reshape industries, drive innovation, and enhance the quality of life for billions of people around the world. 5G communication networks represent a paradigm shift in the way we connect and communicate. With their advanced architecture, dynamic management capabilities, and transformative potential, 5G networks are poised to unlock new opportunities, accelerate digital transformation, and usher in a new era of connectivity and innovation.

INTELLIGENT MONITORING AND PROTECTION SYSTEM OF CRITICAL INFRASTRUCTURE BASED ON MOBILE COMMUNICATION-INFORMATION SYSTEM WITH ELEMENTS OF ARTIFICIAL INTELLIGENCE

The paper proposes innovative modern solutions for an integrated mobile monitoring system with elements of artificial intelligence in the Internet environment based on a mobile communication-information system for collecting, aggregating, processing, and presenting in realtime streams of information objects. It presents the management of communication networks to optimize the transport and processing of information objects based on discrete-event data flow representation and modeling with a hierarchical structure. Qualitative aspects are considered for networks designed for monitoring and notification of geophysical, climatic, and other natural phenomena as well as for anthropogenic systems. A comparison of centralized and decentralized management capabilities is made, as well as the state of the art of network technologies and the possibilities for practical implementation in different network architectures.

Analysis of some weather variables' impacts on UHF and VHF receivers in Yenagoa Southern, Nigeria

In this study, the effects of some important meteorological variables, such as temperature, relative humidity, and mean sea level pressure, were assessed on the Ultra High Frequency (UHF) radio signals from Niger Delta Television (NDTV), transmitting at 743.25 MHz UHF, and the Very High Frequency (VHF) radio signals from Nigerian Television Authority (NTA), transmitting at 189.25 MHz VHF, both in Yenagoa, Southern Nigeria, within 6°20'17"N and 5°37’32″E. The Digital Community- Access/Cable Television (CATV) analyzer was used to measure the radio signal strength from NDTV and the radio signal strength from NTA Yenagoa, and a self-implemented weather monitoring device was used to measure the weather variables simultaneously at an equal distance within the Metropolis. Throughout the entire year of 2022, measurements were made continuously every six hours (respectively between 12 am and 1 am, 6 am and 7 am, 12 pm and 1 pm, and 6 pm and 7 pm local time). The results showed that the radio signals from both UHF and VHF television stations were directly proportional to temperature, inversely proportional to relative humidity, and did not follow any specific pattern of proportionality with mean sea level pressure. Inferentially, it was seen that these meteorological factors mostly affected the radio signals from the UHF television station, and that these impacts were stronger during the months with high relative humidity compared to the months with lower relative humidity. But based on these findings, a postulation has been put out that has to be verified further. These findings and the proposed postulation, it is thought, would undoubtedly be beneficial in supporting and directing the management of radio communication networks for planning and other reasons.

Railway Cyber-Security in the Era of Interconnected Systems: A Survey

Technological advances in the telecommunications industry have brought significant advantages in the management and performance of communication networks. The railway industry is among the ones that have benefited the most. These interconnected systems, however, have a wide area exposed to cyberattacks. This survey examines the cybersecurity aspects of railway systems by considering the standards, guidelines, frameworks, and technologies used in the industry to assess and mitigate cybersecurity risks, particularly regarding the relationship between safety and security. To do so, we dedicate specific attention to signaling, which fundamental reliance on computer and communication technologies allows us to explore better the multifaceted nature of the security of modern hyperconnected railway systems. With this in mind, we then move on to analyzing the approaches and tools that practitioners can use to facilitate the cyber security process. In detail, we present a view on cyber ranges as an enabling technology to model and emulate computer networks and attack-defense scenarios, study vulnerabilities’ impact, and finally devise countermeasures. We also discuss several possible use cases strongly connected to the railway industry reality.

Discrete practical RF energy harvesting models for wireless communication networks

AbstractOptimal resource management in wireless communication networks is usually modeled in discrete state space, for example, using Markov decision process. This requires the energy harvesting (EH) model to be discrete. Since the existing discrete EH models in the literature use simple discrete distributions, for example, Bernoulli and Poisson, with no regard to the properties of the EH circuits, the proposed work handles this dilemma by deriving discrete EH models based on the practical continuous radio frequency to direct current (RF‐to‐DC) power conversion models. General closed‐form expressions are derived for the probability density functions of the harvested energy in the case of linear, piecewise linear, and non‐linear EH models. These general closed‐form expressions are applied to some special fading models, for example, the Nakagami‐ fading model. Simulation results verify the derived models. Moreover, statistical analysis is performed to compare the derived models to other discrete models in the literature. Finally, the derived models are applied to a case study where the effect of the number of discrete EH levels and the importance of the derived models compared to other models in the literature are presented. These comparisons show the deviation in the performance of the discrete models commonly used in the literature from the actual performance when the proposed piecewise linear EH model is used. Based on these comparisons, the proposed models, especially the piecewise linear EH model with an adequate number of segments, are recommended to be used for accurate discrete modeling of the EH process.

Design of security management model for communication networks in digital cultural consumption under Metaverse – The case of mobile game

Under Metaverse, security management of communication networks in digital cultural consumption has attracted more and more attention. As we know, security risks of communication networks caused by non-human factors are becoming weaker and weaker due to technology optimization. Meanwhile, players play significant and increasing role in communication networks. Taking mobile games as an example, this paper designed and applied the security management model to improve security management of communication networks in digital cultural consumption under Metaverse. The results show that: (1) The physiological, psychological, pathological, pharmacological, physical and psychosocial factors exert tremendous influence on communication networks. The psychosocial factors are more prominent. (2) There is security threshold in communication networks. It means that the security state can be entered if security threshold is crossed. This paper designs security threshold as the III level (5.5 ≤ P < 6.5). (3) This paper proposes 3E rules (That is, engineering, education, enforcement). It is beneficial to deal with security risks in communication networks. (4) After two security evaluations, the result is G2 = 6.70 > G1 = 5.01. This confirms the effectiveness of the security management model.

Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, signal interference). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then, it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area.

Preface

The International Conference on Emerging Electronic & Automation Technology (ICEEAT 2022) is a forum for promoting, discussing and developing ideas in emerging electronic and automation technology. ICEEAT 2022 was successfully held virtually, featuring two days of oral presentations with distinguished invited talks. We are very much aware that one of the objectives of worldwide researchers is to gain and share knowledge. However, due to the recent outbreak of Covid-19, we realize that some precaution needs to be made to maintain the balance of the need for sharing our knowledge and the precaution due to global health concerns. Thus, we offer to hold the conference under a virtual platform with the same quality as offline medium. Through ethical virtual conferences, we ensured that the programs would be conducted as planned.The conference agenda covered keynote speeches, oral presentations, and online Q&A discussion. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers selected were presented by their authors one by one. During the conference, we were greatly honored to invite five sophisticated keynote speakers to address their speeches: Prof. Dr. Junle Qu from Shenzhen University (China); Professor Dr. Yiming Li from Zhejiang Normal University (China); Professor Dr. Xiaodong Zhang from Tianjin University (China); Senior; and Assistant Prof. Dr. William Hurst from Wageningen University (Netherlands) and Prof.Dr. Hai Tao from Qiannan Normal University for Nationalities,(China). The wonderful and dramatic speeches of each keynote speaker had triggered heated discussion. Moreover, every participant praised this conference for disseminating useful and insightful knowledge.Scientists and practitioners submitted more than 80 papers in total. The editors and committees to make a success of ICEEAT2022 carefully reviewed all of them. As a result, the conference proceedings were compiled. These papers feature but are not limited to the following topics: Image processing and patron recognition; Artificial intelligent for smart system, communication network control and management, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.On behalf of the conference organizing committee, we would like to express our heartfelt appreciation to all the keynote speakers, peer reviewers, and all the participants. Especially, we would like to acknowledge the Journal of Physics: Conference Series, for the efforts of all the colleagues in publishing this paper volume. We firmly believe that all the attendees have had fruitful discussions and gained valuable knowledge, and will enjoy the opportunity for future collaborations.Committee of ICEEAT2022List of Conference Organization, Committee member is available in this Pdf.

The Smart Network Management Automation Algorithm for Administration of Reliable 5G Communication Networks

Smart network management automation (SNMA) is a technology that enables the automation of network management tasks. It provides a platform for automating network management, allowing administrators to manage their networks more reliably and efficiently. It provides the tools to monitor and manage network performance, identify and troubleshoot problems, and automate the deployment of new services and applications. SNMA also allows for scalability to meet the needs of any organization, regardless of size or complexity. With SNMA, network administrators can quickly and accurately identify problems, increase network uptime, and reduce time spent on manual tasks. The SNMA provides the ability to adjust configurations quickly and respond to changes in the network environment. It can help reduce costs and improve the efficiency of network operations. This paper proposed an intelligent network management automation algorithm for network administration and management in 5G communication networks. The proposed algorithm achieved 91.82% of remote network administration, 95.25% of global network administration, 96.59% of urban network administration, and 95.07% of local network administration. It creates a single submission to the network to make configuration changes, register network resources, manage users’ IP addresses, and filter packets to ensure information security and other tasks.

Practical Training of Students as a Component оf the Educational and Professional Space of the Institution оf Higher Education: Traditions and Innovations

The purpose of the work is to outline the concept of "educational and professional space of a higher education institution" and characterize its content. The study focuses on the educational and professional programs "Documentology, management of state authorities and local self-government" and "Management of social and communication networks, Internet marketing" of the specialty "Information, library and archival affairs", which are implemented by the Kyiv National University of Culture and Arts. The methodology of the work consists in the analysis and synthesis of the scientific works of the authors on the research of the outlined problem, a hypothetical-deductive method for outlining the content of the concept of "educational and professional space of a higher education institution" and identifying its main components. The scientific novelty of the study consists in characterizing the content of the concept of "educational and professional space of a higher education institution", identifying its system-forming components – content and organizational and communicative, and delineating its components both traditional (types of practice as a structural component of the educational process of training a specialist), and innovative - dual education for the training of specialists in the specialty "Information, library and archival affairs". Conclusions. Today, the educational space of a higher education institution is moving to a new qualitative level and should be defined as an educational and professional space in which subject-subject interaction of potential employers with students, representatives of higher education institutions takes place, not only from the point of view of established forms of practice as a structural component of educational process, and is carried out in the implementation of an innovative form of educational and professional training - dual education. Practice as a structural component of the educational process corresponds to the initial stage of professionalization and is a process of involving an individual in professional activity and improvement in one of the directions of this activity, is a means of adapting students to professional activity, contributes to the development of professional culture, innovativeness of professional activity, development of professional values, identification of behavior with the norms of professional ethics. The introduction of dual education for the innovative specializations "Documentology, management of state authorities and local self-government bodies" and "Management of social and communication networks, Internet marketing" with the specialty "Information, library and archival affairs" is due to the infrastructure of document-information and social-communication types activities and will ensure the integration of education, science and practical activities in the document and information field. Keywords. Educational and professional space, institution of higher education, dual education, information, library and archival affairs.

DFF-SC4N: A Deep Federated Defence Framework for Protecting Supply Chain 4.0 Networks

The management of contemporary communication networks of supply chain (SC) 4.0 is becoming more complex due to the heterogeneity requirements of new devices concerning the integration of the Internet of Things in the legacy industry networks. Hence, it becomes a challenging task to secure networks of SC 4.0 from cyber-attacks and provide a robust and efficient defence framework that can resist sophisticated attacks. Machine learning-based intelligent detection algorithms are often trained at either a centralized or single server, which makes it difficult to train an effective model and also it violates privacy concerns if gathering data from other servers at the edge. Classical machine learning approaches function on the legacy group of data placed on a central or single server, which brands it the least favored choice for supply chain networks, with data privacy issues. To address these problems, this article proposes a federated learning-based efficient detection model named, DFF-SC4N, to proactively identify intrusions from SC 4.0 networks using distributed local data training. DFF-SC4N uses communication rounds in a federated learning manner having gated recurrent units by only sharing the learned parameters and keeps the data intact on local servers. The accuracy of the global model is optimized by an aggregating model, which updates from multiple servers and multiple SC 4.0 networks. Extensive experiments on real industrial network data demonstrate that the DFF-SC4N outperforms both centralized training models and state-of-the-art peer methods in protecting SC 4.0 networks.

Decentralized Renewable Resource Redistribution and Optimization for Beyond 5G Small Cell Base Stations: A Machine Learning Approach

Optimal resource provisioning and management of the next generation communication networks are crucial for attaining a seamless quality of service with reduced environmental impact. Considering the ecological assessment, urban and rural telecommunication infrastructure is moving toward deploying green cellular base stations to cater to the needs of the ever-growing traffic demands of heterogeneous networks. In such scenarios, the existing learning-based renewable resource provisioning methods lack intelligent and optimal resource management at the small cell base stations (SCBS). Therefore, in this article, we present a novel machine learning-based framework for intelligent resource provisioning mechanisms for micro-grid connected green SCBSs with a completely modified ring parametric distribution method. In addition, an algorithmic implementation is proposed for prediction-based renewable resource re-distribution with energy flow control unit mechanism for grid-connected SCBS, eliminating the need for centralized hardware. Moreover, this modeling enables the prediction mechanism to estimate the future on-demand traffic provisioning capability of SCBS. Furthermore, we present the numerical analysis of the proposed framework showcasing the systems’ ability to attain a balanced energy convergence level of all the SCBS at the end of the periodic cycle, signifying our model’s merits.

A Novel Approach for Integrated Shortest Path Finding Algorithm (ISPSA) Using Mesh Topologies and Networks-on-Chip (NOC)

A novel data dispatching or communication technique based on circulating networks of any network IP is suggested for multi data transmission in multiprocessor systems using Networks-On-Chip (NoC). In wireless communication network management have some negatives have heavy data losses and traffic of data sending data while packet scheduling and low performance in the varied network due to workloads. To overcome the drawbacks, in this method proposed system is Integrated Shortest Path Search Algorithm (ISPSA) using mesh topologies. The message is sent to IP (Internet Protocol) in the network until the specified bus accepts it. Integrated Shortest Path Search Algorithm for communication between two nodes is possible at any one moment. On-chip wireless communications operating at specific frequencies are the most capable option for overcoming metal interconnects multi-hop delay and excessive power consumption in Network-on-Chip (NoC) devices. Each node can be indicated by a pair of coordinates (level, position), where the level is the tree's vertical level and the view point is its horizontal arrangement in the sequence of left to right. The output gateway node's n nodes are linked to two nodes in the following level, with all resource nodes located at the bottommost vertical level and the constraint of this topology is its narrow bisection area. The software Xilinx 14.5 tool by using that overall performance analysis of mesh topology, each method are reduced data losses with better accuracy although the productivity of the delay is decreased by 21 % was evaluated and calculated..

Efficient resource management in 6G communication networks using hybrid quantum deep learning model

The sixth generation (6G) wireless communication network is expected to provide wide-ranging coverage, short latency, cost effectiveness, lower power consumption, and a high level of security. The Quality of Service (QoS) can be improved with the help of proper resource management by utilizing Artificial Intelligence and Machine Learning procedures. The proposed Hybrid Quantum Deep Learning (HQDL) model which comprises of Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). The CNN achieves network reconfiguration, resource distribution and slice collection while RNN is utilized for error proportion, load balancing etc. The future model QoS performance is evaluated by utilizing numerous indefinite devices, slice features and congestion environments. The proposed model achieved an overall accuracy of 97.16% that replicates its pertinence.