Communication Network Model Research Articles

Research on Over-the-Horizon Perception Distance Division of Optical Fiber Communication Based on Intelligent Roadways.

With the construction and application of more and more intelligent networking demonstration projects, a large number of advanced roadside digital infrastructures are deployed on both sides of the intelligent road. These devices sense the road situation in real time through algorithms and transmit it to edge computing units and cloud control platforms through high-speed optical fiber transmission networks. This article proposes a cloud edge terminal architecture system based on cloud edge cooperation, as well as a data exchange protocol for cloud control basic platforms. The over-the-horizon scene division and optical fiber network communication model are verified by deploying intelligent roadside devices on the intelligent highway. At the same time, this article uses the optical fiber network communication algorithm and ModelScope large model to model inference on real-time video data. The actual data results show that the StreamYOLO (Stream You Only Look Once) model can use the Streaming Perception method to detect and continuously track target vehicles in real-time videos. Finally, the method proposed in this article was experimentally validated in an actual smart highway digital infrastructure construction project. The experimental results demonstrate the high application value and promotion prospects of the fiber optic network in the division of over the horizon perception distance in intelligent roadways construction.

A Multi-Layer Information Dissemination Model and Interference Optimization Strategy for Communication Networks in Disaster Areas

A Multi-Layer Information Dissemination Model and Interference Optimization Strategy for Communication Networks in Disaster Areas

Design of Intelligent City Communication Network Based on Internet of Things

Abstract In response to the advancements in Internet of Things (IoT) technology and the growing demands for data transmission, there is a crucial need for further integration of communication network technology with IoT to enhance efficiency. This paper presents the design of a multi-node communication network model utilizing the Orthogonal Frequency-Division Multiplexing (OFDM) algorithm. In the realm of physical layer parameter design, uplink, and downlink carriers are allocated based on OFDM symbols, employing the Zadoff-Chu (ZC) sequence as the pilot frequency sequence. Additionally, the Cyclic Redundancy Check (CRC) error-checking code is implemented to ensure the accuracy of network transmissions. The model proposed in this study is applied to the operational dynamics of traffic systems within a smart city, focusing on the security and operational efficiency of the traffic communication network. Empirical testing and analysis demonstrate that the communication network, as designed, is susceptible to malware attacks by 25.68%, representing the least vulnerability among the three algorithms assessed. Moreover, in examining the bus trip time distribution with the implementation of this model, the average running time is predominantly around 11.76 minutes, which is a reduction of 0.74 minutes compared to previous configurations, thereby indicating an improvement in operational efficiency.

Retracted: Optimization and Simulation of Controller Area Network Communication Model Based on Industrial Internet of Things Platform

Retracted: Optimization and Simulation of Controller Area Network Communication Model Based on Industrial Internet of Things Platform

Combining the Information Diffusion Model for the Propagation of Zhouyi Ideology in the New Era

Abstract This paper examines the moral edification ideas in Zhouyi and looks at the contemporary value of Zhouyi ideology and ideology. The UAU-SIS multiple network communication model is constructed, and the information radiation model between cyberspace and real space is proposed, which has a positive impact on the dissemination of Zhouyi ideology. The Master differential equation of the degree distribution of spatial topological structure is established to provide a basis for the dissemination of information related to Zhouyi ideology. Analyze the communication carriers of Zhouyi thought and improve the communication effect of Zhouyi thought in the new era by combining it with information diffusion technology. Simulation experiments are used to examine the feasibility and performance of the proposed algorithm. Investigate the influence of Zhouyi’s thought dissemination on citizens’ moral education consciousness. The experiment shows that the diffusion range of the algorithm is improved from 100 to 600 after 16 iterations, and the increased speed decreases to 4879.13s, the dissemination effect is better, and the algorithm is feasible. The scores of 6 evaluation indexes of students’ moral edification are 0.76, 0.93, 0.71, 0.63, 0.91, and 0.53. The overall level is good, and there is still room for improvement in the awareness of moral edification. It can be seen that the spread of Zhouyi thought has a beneficial effect on the citizens’ consciousness of moral enlightenment.

Stability analysis and L1-gain characterization of positive sampled-data systems

In this study, the positive stability and [Formula: see text]-gain characterization problem are investigated for positive sampled-data systems. By constructing a new sampling-period-dependent copositive Lyapunov functional, and introducing the free weight matrix method to remove the single integral term, sufficient conditions for the asynchronous stability of the positive sampled-data system are established. Furthermore, system disturbances that occur frequently in engineering are taken into account. Conditions for robust stability of positive sampled-data systems that satisfy [Formula: see text]-gain performance are developed in the form of the linear programming technology. A numerical example and a practical simulation based on a communication network model are provided to illustrate the rationality and application of the theoretical results.

A Quantum-Behaved Heterogeneous Topology Optimization Model For Optical Wireless Communication Networks

A Quantum-Behaved Heterogeneous Topology Optimization Model For Optical Wireless Communication Networks

Complex quantum network models from spin clusters

In the emerging quantum internet, complex network topology could lead to efficient quantum communication and robustness against failures. However, there are concerns about complexity in quantum communication networks, such as potentially limited end-to-end transmission capacity. These challenges call for model systems in which the impact of complex topology on quantum communication protocols can be explored. Here, we present a theoretical model for complex quantum communication networks on a lattice of spins, wherein entangled spin clusters in interacting quantum spin systems serve as communication links between appropriately selected regions of spins. Specifically, we show that ground state Greenberger-Horne-Zeilinger clusters of the two-dimensional random transverse-field Ising model can be used as communication links between regions of spins. Further, the resulting quantum networks can have complexity comparable to that of the classical internet. Our work provides a generative model for further studies towards determining the network characteristics of the emerging quantum internet.

Data-efficient GNN models of communication networks using beta-distribution-based sample ranking

Machine learning models for tasks in communication networks often require large datasets to be trained. This training is cost intensive, and solutions to reduce these costs are required. It is not clear what the best approach to solve this problem is. Here we show an approach that is able to create a minimally-sized training dataset while maintaining high predictive power of the model. We apply our approach to a state-of-the-art graph neural network model for performance prediction in communication networks. Our approach is limited to a dataset of 100 samples with reduced sizes and achieves an MAPE of 9.79% on a test dataset containing significantly larger problem sizes, compared to a baseline approach which achieved an MAPE of 37.82%. We think this approach can be useful to create high-quality datasets of communication networks and decrease the time needed to train graph neural network models on performance prediction tasks.

Improving resilience of high-speed train by optimizing repair strategies

High-speed trains (HSTs) are used to transport passengers to destinations efficiently and safely. However, when HST units fail, strategies for repairing them are not available. Hence, to optimize repair strategies, an interdependent network is used to model an HST, and a metric of resilience based on network theory is introduced. Based on practical factors, an interdependent machine–electricity–communication network (IMECN) and related cascading failure models are developed. Comprehensive robustness metrics for the IMECN and nodes are proposed considering the topology of networks and functional features of nodes. Accordingly, a resilience optimization model of the IMECN subject to disturbances is formulated and solved using a tabu search (TS) algorithm. Finally, a real-world HST is considered to analyze the optimal repair strategy for different numbers of failed nodes. Analysis results show that the recovery process under the optimal repair strategy has at most three stages. When a few nodes and less than half of the nodes fail, the highest and lowest levels of the IMECN are achieved, respectively. Moreover, the characteristics of the preferentially repaired node in terms of importance, vulnerability, network type, and interdependency are analyzed. Results indicate that the optimal repair strategy is not necessarily determined by topological metrics.

Generative adversarial networks based digital twin channel modeling for intelligent communication networks

Generative adversarial networks based digital twin channel modeling for intelligent communication networks

A DRL-Based Load Shedding Strategy Considering Communication Delay for Mitigating Power Grid Cascading Failure

Successive failures in power transmission lines can cause cascading failures in the power grid, which may eventually affect large parts of the power grid and even cause the power grid system to go down. Collecting and transmitting primary equipment information and issuing load-shedding action commands in the power grid depend on the power communication network. With the help of the power communication network, we can better observe the situation of the power grid in real time and provide a guarantee for the regular working of the power grid. However, the communication network also has the problem of communication delay causing latency in load-shedding action. On the premise of preserving the key physical properties and operational characteristics of the power grid, this paper uses the IEEE 14 and 30 bus systems as examples to establish a direct current (DC) power flow simulation environment. We establish a communication network model based on the power grid topology and the corresponding communication channels. For the problem of cascading failures occurring in the power grid after transmission line failures, a load-shedding strategy using soft actor-critic (SAC) based on deep reinforcement learning (DRL) was developed to effectively mitigate cascading failures in the power grid while considering the impact of communication delay. The corresponding communication delay is obtained by calculating the shortest communication path using the Dijkstra algorithm. The simulation verifies the feasibility and effectiveness of the SAC algorithm to mitigate cascading failures. The trained network can decide on actions and give commands quickly when a specific initial failure is encountered, reducing the scale of cascading failures.

Performance Evaluation of Communication Infrastructure for Peer-to-Peer Energy Trading in Community Microgrids

With the rapidly growing energy consumption and the rising number of prosumers, next-generation energy management systems are facing significant impacts by peer-to-peer (P2P) energy trading, which will enable prosumers to sell and purchase energy locally. Until now, the large-scale deployment of P2P energy trading has still posed many technical challenges for both physical and virtual layers. Although the communication infrastructure represents the cornerstone to enabling real-time monitoring and control, less attention has been given to the performance of different communication technologies to support P2P implementations. This work investigates the scalability and performance of the communication infrastructure that supports P2P energy trading on a community microgrid. Five levels make up the developed P2P architecture: the power grid, communication network, cloud management, blockchain, and application. Based on the IEC 61850 standard, we developed a communication network model for a smart consumer that comprised renewable energy sources and energy storage devices. Two different scenarios were investigated: a home area network for a smart prosumer and a neighborhood area network for a community-based P2P architecture. Through simulations, the suggested network models were assessed for their channel bandwidth and end-to-end latency utilizing different communication technologies.

Structural models based on spectral graph theory for land-based digital communication networks

Problem statement. For the organization of digital communication networks in the range of meter and decimeter waves, as well as when providing communications based on them in hard-to-reach areas, problems arise related to maintaining the stability and continuity of information exchange between the switching nodes of the ground infrastructure. The existing approaches to solving these problems are based on the use of means of retransmission using radio relay stations and individual flight-lifting equipment. However, with limited deployment time, in emergency situations and when performing special tasks, these approaches are unacceptable for a number of objective reasons. Therefore, the search for ways to improve digital communication networks in the range of meter and decimeter waves is relevant and has important scientific and practical significance for ensuring the stability and continuity of information exchange by ground switching nodes in a given area. Purpose. To develop a structurally stable model of digital communication networks in the range of meter and decimeter waves based on a group of unmanned aerial vehicles, ensuring the organization of continuous information exchange of ground switching nodes in a given area for the required period of time. Results. Based on the results of the research, practical recommendations are formulated for their use to solve the problems of organization, restoration and redundancy, as well as the operational build-up of digital communication networks in the range of meter and decimeter waves for a given time interval in unfavorable operating conditions. The prospects for further research in the organization of the structural contour of the complex multi-circuit adaptation of digital communication networks for various purposes are determined. Practical significance. The results can be used by specialists in the field of satellite radio navigation in the development of local navigation systems.

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.

Hierarchical Deep Reinforcement Learning for Self-Powered Monitoring and Communication Integrated System in High-Speed Railway Networks

To align with the vision of future intelligent high-speed railway (HSR) networks, integrating sensor monitoring and remote communication are challenging for ensuring the lightweight of train equipment, high-quality transmissions, and dynamic interaction between monitoring and communication. In this paper, we propose a self-powered multisensor monitoring and communication integrated system in HSR. A low-power backscatter communication working framework of the self-powered monitoring system is designed in the monitoring network model, and a finite Gaussian mixture model (GMM) clustering method is used to analyze the communication cell coverage area in the communication network model. Aiming to minimize the total task completion time, we formulate a data monitoring and remote communication problem with the energy transfer constraint, data collection constraint, and transmission data rate constraint. As for the non-convex minimum time optimization problem, we develop a novel option-based hierarchical deep reinforcement learning (OHDRL) method to deal with the complex continuous variation characteristics of the monitoring and communication integrated HSR system. The system learns to select options at a high level, and the action is executed according to the policy of the selected option at a low level. This approach enables us to handle stochastic HSR environments, closed-loop policies, and goals in a temporal abstraction way. Numerical results reveal that the proposed algorithm for the integrated monitoring and communication HSR achieves a significantly higher reward and more stable learning performance than other algorithms in the literature.

Results of simulation modeling of technical parameters of a multiservice network

In a scientific article, the authors presented the results of simulation modeling of the technical parameters of a multiservice network. As a model of a multiservice communication network for the computational experiment, the model proposed in the previous scientific publications of the author was chosen. The selected model uses the Laplase-Stilles transform. Simulation modeling was carried out using the technical parameters of the multiservice network’s availability factor and network load. Scientific results of experimental research work are given in the form of tables. Relations of the probability of betime servicing of an application on the load of a multiservice communication network for various availability factors and the probability of untimely service of an application on the availability factor for numerous network loadings are obtained. The character of the distribution of costs necessary for the implementation of solutions for different categories of technical operation is shown. Scientific research on determining the objective function’s minimum value is presented in graphs and diagrams. The results of simulation modeling of the technical parameters of multiservice networks are presented in the form of diagrams using the Matlab software environment.

Statistical channel model for 6G communication networks in Banda Aceh City

Wireless technology is expected to undergo considerable transformation because of the numerous services offered by 6G communication networks, which virtually entirely encompass every part of everyday life and use a variety of devices. Channel modeling is an essential factor in designing 6G communication networks. To meet the channel requirements of future 6G communication networks, it is crucial to measure the channel to consider path loss, multi-band, fading, blocking effect, multipath clustering, transmitter, and receiver moving speed/direction/time. The goal of this paper is to design and evaluate a 6G communication network in Banda Aceh City using a statistical channel model. The channel model is associated with environmental conditions such as rainfall and humidity. The method is then based on computer simulation using the NYUSIM simulator to complete the channel modeling using an operating frequency of 95 GHz with a bandwidth of 800 MHz. In the simulator, the designed 6G channel model is evaluated in both line-of-sight (LOS) and non-line-of-sight (NLOS) network environments. In addition, the designated network parameters, such as the coverage area, angle of arrival (AoA), angle of departure (AoD), and power delay profile (PDP), are simulated. The results, at AoA, the value of received power for LOS conditions ranges from -86 dBm to -101 dBm, while the value for NLOS conditions ranges from -91 dBm to -111 dBm. Under LOS conditions, the received power for AoD ranges from -86 dBm to -101 dBm, whereas under NLOS conditions, it ranges from -91 dBm to -111 dBm. In the omnidirectional PDP, the pathloss value for the LOS condition is 99.8 dB and the delay is 17.9 ns, while the pathloss value for the NLOS condition is 104.2 dB with a delay of 28.1 ns. For the directional PDP, the LOS condition yields a path loss of 106.4 dB and a delay of 2.9 ns, while the NLOS condition yields a path loss of 110.5 dB and a delay of 3 ns. Conclusions. The simulation indicated that the AoA, AoD, and PDP in terms of received power, pathloss, and propagation delay are in acceptable conditions for a 6G network in Banda Aceh City in the two observed environments. Therefore, it is conceivable to establish a 6G network in Banda Aceh City in the future.

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.