Flood disasters pose severe challenges to communication infrastructure, necessitating robust and adaptive emergency communication solutions. Traditional methods, such as satellite and drone-based systems, often face limitations in cost, coverage, and environmental resilience. This study explores the application of Active Reconfigurable Intelligent Surfaces (Active RIS) in flood scenarios, leveraging their dynamic signal optimization capabilities to overcome water-induced signal attenuation and complex propagation obstacles. By integrating programmable electromagnetic units with active amplification, Active RIS enables real-time phase and amplitude adjustments, enhancing signal stability and coverage. Experimental validation demonstrates its effectiveness in maintaining connectivity for both static and mobile users, even in obstructed environments. The technology supports rapid deployment and energy-efficient operation, making it a scalable solution for disaster-resilient communication. This work highlights Active RIS as a transformative approach to address critical gaps in emergency communication during floods, offering a reliable framework for real-time coordination and resource allocation in disaster response.

As the second strength network of the power grid, the power communication network is the cornerstone for maintaining the safe and stable operation of the grid. Identifying important nodes to the power communication network and protecting them redundantly can increase its risk resistance. In this paper, on the basis of complex network theory, the structure hole algorithm is improved by link importance to enable it to identify multi-layer network topology, overcoming the shortcomings of the traditional structure hole algorithm that cannot accurately identify bridging nodes and establishing an important node identification model (ILS) by combining the characteristics of power communication networks. The results show that ILS can effectively identify key nodes in power communication networks.

BeiDou Navigation Satellite System (BDS) has effectively improved management efficiency and significantly reduced operating costs in the intelligent agricultural production, smart traffic management and epidemic prevention. Based on the research of BeiDou technology, an intelligent condition monitoring mode of power system is proposed for the demand of environmental condition monitoring of power transmission lines and substation equipment, combined with big data and artificial intelligence technology. The monitoring effectiveness of transmission and substation application scenario monitoring mode is elaborated to provide useful reference for condition monitoring.

With the continuous development of the scale of the power grid, the power communication network, as the second network of the power grid, its topology structure is constantly complicated safe and reliable operation. In this paper, the load quantification model of the network topology layer is established by using betweenness centrality from the perspective of network topology, and the load quantification model of the power business layer is established by using business importance from the perspective of power business. In the physical layer, the physical layer load quantification model is established by using the reliability of the optical cable. The TOPSIS method is used to obtain the comprehensive load quantification model of the power communication network. Finally, the simulation model is constructed and compared with other load quantification models to prove the accuracy of the proposed method. The research conclusions have certain guiding significance for the load quantification, risk protection and network optimization of the power communication network.

With the development of various vertical industry services such as autonomous driving, energy Internet, and smart cities, mobile communication networks need to provide users with ubiquitous high-speed access while using limited network resources to provide differentiated and customized services, the 5G network satisfies the requirement of the future. However, for the access network, there are many types services accessing the network. In order to provide users with diverse personalized services, network slicing scheme is introduced into 5G network. Network slicing is based on the technology of network function virtualization, which can establish multiple virtual private networks in the device according to the needs of users. Each slice is a private network, and different virtual networks are kept isolated from each other. This article studies the access network of the 5G network, in order to ensure the quality of user access, we study the mapping scheme of network slices and NFV to ensure the communication quality of access networks of different user types. Finally, we perform some simulations to verify the proposed method, and the result shows that our proposed can ensure the communication quality for the users which connect into the 5G network.

In this paper, the optical fiber sensing technology is taken as the research object. Firstly, according to the demand of the sensing layer of the power Internet of things, a transmission line environment online monitoring system is proposed based on the multi risk monitoring of external breakage, fire, icing, galloping and so on of the cable (overhead line), the composition and deployment of the monitoring system are introduced. Secondly, based on the research of temperature field and stress field distribution of optical fiber, combined with big data and artificial intelligence, the important environmental parameters of the line can be monitored. Finally, taking different monitoring scenarios as examples, this paper expounds the different monitoring functions of the environmental monitoring system of the Internet of things for transmission lines, which provides useful reference for environmental monitoring of transmission lines.

The essence of optical fiber communication transmission technology is to use light as the carrier of information, and to achieve the purpose of information transmission through the optical fiber network. The communication mode based on optical fiber communication technology has been widely recognized in the field of industrial production and telecommunication transmission due to its good information transmission performance. However, with the expansion of the construction scale of optical fiber communication network and the improvement of user coverage, the disadvantages of the management and maintenance of the traditional optical fiber communication network are becoming more and more obvious. Therefore, this paper describes the characteristics of the optical fiber communication technology, and analyses the existing problems in the optical communication system and their causes. On this basis, this paper analyses the automatic operation and maintenance technology of optical fiber communication system. The analysis results in this paper provide some technical support for the deep integration of computer control technology and optical fiber communication automatic maintenance system.

In recent years, the basic business of power distribution production, such as distribution automation, power consumption information collection and load control, distributed power supply and so on, is growing rapidly, and the demand for power terminal communication access is increasing. Based on the analysis and research of terminal communication access technology, this paper proposes a heterogeneous network combination scheme which integrates multiple terminal communication technologies to solve the problems such as the limitation of existing terminal communication access network technology on the power distribution side. Heterogeneous network networking technology realizes the integration application of wireless private network, low-power wide area network, medium voltage broadband carrier and other channels. It can give full play to the advantages of various communication modes, improve the communication guarantee ability, meet the requirements of full coverage of power terminal communication access network business, provide strong channel support for terminal information return, and provide communication system construction for distribution terminal Useful reference.

In power line communications, network traffic has the highly dynamic inherent nature. This leads to the huge challenge to accurately predict and estimate them. Accordingly, this results in new problem for power communication networks' traffic engineering and power scheduling task. This paper proposes a novel reconstruction approach to estimate dynamic traffic in surface wave-based power line communications based on the Generalized Regression Neural Network (GRNN) theory. Firstly, we use the GRNN theory to describe time-varying network traffic features. Then by considering the spatio-temporal correlation feature of network traffic, we construct the dynamic GRNN model to describe network traffic. Thirdly, we propose a new traffic reconstruction algorithm to estimate and predict network traffic in power line communications, based on the proposed model. Thus based on the proposed method, we can quickly estimate network traffic and obtain accurate estimation values. Simulation results show that our approach is feasible.

With networks technologies and service applications fast advancing, power telecommunication network traffic has some novel features, but existing methods are very difficult to capture them, especially for abnormal network traffic. This paper proposes an autoregressive moving average model (ARMA) based abnormal identification approach for remote smart scheduling of optical fiber cores. Firstly, we use the ARMA theory to characterize network behaviors for remote smart scheduling of optical fiber cores. Secondly, the ARMA model about network traffic in remote smart scheduling of optical fiber cores is constructed to describe network traffic. Thirdly, based on the proposed model, we use the identification analysis theory to diagnose the hidden abnormal parts in these applications. Then we propose an anomaly extraction algorithm to extract the abnormal and anomalous part from network traffic for these power telecommunication applications. Simulation results show that the proposed approach is promising.

With the development of communication networks, a lot of new applications emerge in the power telecommunication access networks, which have many new features and properties of the network traffic. These features are important for modeling the network traffic in the network-level. This paper propose a new feature extraction and network traffic model method. Firstly, we analyze the features of network traffic in time-frequency domain. Then, we use discrete wavelet transform to exploit the features of network traffic in the time domain and frequency domain. We run multi-fractal discrete wavelet transform (MDWT) for network traffic to decompose them into different frequency component and train an artificial neural network to predict the low- and high-frequency components of network traffic, and use them to reconstruct the network traffic. Finally, in order to validate our network traffic model, we conduct the network traffic prediction on the actual data. Simulation results show that our approach is feasible.

Currently power telecommunication access networks have many new requirements to meet the low-power WAN with smart electric power allocations. In such a case, network traffic in the low-power WAN has exhibited new features and there are some challenges for network managements. This paper uses the linear regression model to propose a new method to model and predict network traffic. Firstly, network traffic is modeled as a linear regression model according to the regression model theory. Then the linear regression modeling method is used to capture network traffic features. By calculating the parameters of the model, it can be decided correctly. Then, we can predict network traffic accurately. Simulation results show that our approach is effective and promising.

Network traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, network operators need to perform the accurate and real-time acquirement for network traffic. However, to quickly and accurately estimate network traffic is an open problem in current communication networks including power communication networks. This paper propose an accurate acquirement approach to obtain network traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end network traffic in power telecommunications. Secondly, based on the STFT method, network traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model network traffic. The corresponding model method is used to construct and capture network traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain network traffic. Simulation results show that our approach is promising.

The power wireless private network is a wireless broadband access system that is deeply customized for the development of smart grid terminal communication access, and is integrated with the power dedicated 230 MHz spectrum to meet the wide coverage, large capacity, strong real-time performance, high security, flexibility and easy-to-expand of the power intelligent terminal, and provide integrated wireless communication private network solutions for various services such as distribution automation, electrical information collection, and precise load control. This paper proposes a data collection node planning algorithm in wireless power private network, which can realize the data measurement of large-scale intelligent power terminals at a small cost. Simulation results show the reliability of our algorithm.

Network traffic is significantly difficult be correctly estimated and predicted in power line communications because network traffic has obvious dynamic features. How to accurately estimate and predict network traffic in power communication networks is very significant for power scheduling. This paper studies the network traffic estimation problem in power line telecommunications and proposes a new estimation method to accurately forecast power communication network traffic. Firstly, we use the Hibert-Huang transform theory to capture network traffic features in power line communications. Secondly, we construct the Hibert-Huang transform estimation model about network traffic in power line communications to forecast dynamic network traffic. Thirdly, we propose a new traffic estimation algorithm to estimate network traffic. Simulation results show that our approach is feasible.

Electric power special optical cable is an important part of electric power information communication system. Electric power special optical fiber cable, can be simply understood as the optical cable and power line belongs to the same tower erection, the optical cable does not need to be set up separately. Including ADSS (ADSS), OPGW (OPGW), winding cable (GWWOP), the phase of optical fiber composite (OPPC) etc.. Electric power special optical cable is in the state of operation, when the fault occurs, it is needed to be repaired immediately. In this paper, the common faults of electric power special optical cable and its analysis methods are discussed, which provide the theoretical support for the operation and maintenance of the optical cable.

This paper studies anomaly detection approaches about access network traffic in power telecommunications. Firstly, we use the Fourier series expansion theory to analyze the features in access network traffic for power telecommunications. Access network traffic is regarded as a discrete time series. Then we perform the Fourier series expansion process for access network traffic. Secondly, according to the Fourier series expansion of access network traffic, we construct the Fourier series expansion matrix. Thirdly, we the principal component analysis theory to decompose the matrix. The hidden properties about access network traffic are dug out. Then we propose a detection algorithm to find out the abnormal part in access network traffic in power telecommunications. Simulation results show that our approach is effective.

The energy efficiency performance of Orthogonal Frequency Division Multiplexing (OFDM) in cellular networks is strongly affected by power allocations and channel allocations due to the inter-cell interference. This paper presents the energy efficiency optimization problem in OFDMbased cellular networks. To the end, an energy efficiency optimization function is constructed, which maximizes energy efficiency and guarantees users' requirement. Since the model proposed is a nonlinear fractional program, which is NP-hard. To solve the model, the paper uses the successive convex appropriate method to appropriate the objective function. And then it uses the Lagrange dual method and sub-gradient to address the sub-optimal problem. Simulation results show that energy efficiency is convergence and maximum energy efficiency performance can be obtained.