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Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of the mobile communication technology and the acceleration of 5G commercial network deployment, energy consumption of 5G, which will continue to raise the operating expense significantly.How to maximize the energy efficiency while ensuring service experience and equipment safety has always been one of the research focus in the industry.With the challenges including complexity of network architecture and variety of base station types, an AI-based energy-saving technology including policy generation and closed-loop security assurance of “perception, prediction, analysis, and decision” was introduced.After calibration and validation based on the offline dataset, the false-switch-off rate is less than 2%, and the recall rate is not fewer than 84%.Further study shows that the technology has greater potential on energy-saving.

With the continuous development and evolution of network technology in recent years, the user’s demand for network resources is becoming larger and larger, and the content is becoming more and more complex.The traditional network architecture is difficult to meet the current flexible network needs.The revolutionary technology of SDN endows the network with programm ability and evolvability, and realizes more flexible management of network resources.In order to improve the utilization of network resources, a traffic prediction agent based on LSTM model was proposed, which provides a theoretical basis for routing planning.By predicting the network traffic in advance, we can take defensive measures before the traffic surge to ensure the security and stability of the network.The function of route planning and lossless path switching based on traffic prediction was realized.

In the context of intensive network construction, how to realize automatic, intelligent, and experienceguaranteed base station energy consumption management has become a pain point for current operators in operation management.A 3G/4G/5G smart base station power saving solution with full intelligent detection, full-scenario modeling, and full-process self-service was innovatively proposed.The coverage scene was distinguished by a dynamic time warping algorithm, and the SARIMA model was used to predict the time frame to build the model dynamically.Monitoring indicators ensured quality of experience, automatically issue power-saving strategies, and promptly raise SMS alerts.In the case that the user had no perception, the energy consumption of the base station was saved to the greatest extent at the cell granularity level.This method had been implemented on a pilot study in a province.The average annual power saving efficiency of a single station in the pilot area can reach 9.24% per day per station, which can be put in actual production.

With the explosive growth of wireless access user demand, 5G network traffic is increasing exponentially and showing a trend of diversity and heterogeneity, which made the network traffic prediction face many challenges.Due to the multi-layer architecture of macro base station, micro base station and pico base station in 5G network, a traffic prediction method based on ge-nerative adversarial network (GAN) is proposed.First, the generation network captures the temporal-spatial features of network traffic and the type features of base station, and then the splicing feature is inputted into the composite residual module to gene-rate the predictive traffic, which is inputted into the discriminant network.Second, the discriminant network determines whether the generative traffic is real traffic or predictive traffic.Finally, after the game confrontation between the generation network and the discriminant network, the generation network could generate high-precision predictive traffic.The experimental results show that, compared with 2DCNN, 3DCNN and ConvLSTM, the two-dimensional root mean square predictive error of GAN is reduced by 58.64%, 38.74% and 34.88%, respectively.Therefore, GAN has the best performance of traffic prediction.

In the scenarios of traffic diagnostic analysis and prediction in optical network based on deep learning, the work of traffic data collection and storage for optical link is limited due to security and other reasons. Aiming at the problem that the amount of traffic data is too small to support deep learning training, this paper proposes an optical network traffic data synthesis algorithm based on topology link recognition. The core idea is to combine the conditional generation model based on optical network topology and the data synthesis model based on optical network traffic in the framework of generative adversarial networks. The traffic data of the given optical link is synthesized in a self-supervised way. The simulation results show that the auto-correlation coefficients of the traffic data synthesized by the proposed algorithm is close to the real data, and the accuracy of the traffic prediction model based on fully connected neural network is more than 95%.

In the scenarios of traffic diagnostic analysis and prediction in optical network based on deep learning, the work of traffic data collection and storage for optical link is limited due to security and other reasons. Aiming at the problem that the amount of traffic data is too small to support deep learning training, this paper proposes an optical network traffic data synthesis algorithm based on topology link recognition. The core idea is to combine the conditional generation model based on optical network topology and the data synthesis model based on optical network traffic in the framework of generative adversarial networks. The traffic data of the given optical link is synthesized in a self-supervised way. The simulation results show that the auto-correlation coefficients of the traffic data synthesized by the proposed algorithm is close to the real data, and the accuracy of the traffic prediction model based on fully connected neural network is more than 95%.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.

Traffic condition prediction is an important part of intelligent transportation system, and traffic flow is the most direct embodiment of traffic condition. Therefore, traffic flow prediction has important application value. On the one hand, the roads in the city have spatial topological properties, on the other hand, the traffic flow changes dynamically with time. Therefore, the key to the prediction of traffic flow is to model the time and space dependence in the data. In view of this characteristic, this paper used neural network model and attention mechanism to explore the temporal and spatial dependence relationship in traffic flow data, and proposed a traffic flow prediction model based on time map attention. In terms of spatial dependence, it used a learning algorithm combining graph convolution network and attention to assign different weights to nodes with different influence degrees, and added node adaptive learning to effectively extract spatial features. In terms of time dependence, it used the temporal convolution network to extract the temporal features, and expanded the sensing domain by expanding convolution, so as to capture the features of longer time series data. A spatial-temporal network layer was composed of graph attention network and time convolution network, which was finally connected to the output layer to output the prediction results. The model used the combination of graph convolution neural network and attention mechanism to extract spatial features, fully considered the spatial relationship between roads, and used temporal convolution network to capture temporal features. After experiments on two real datasets, it is found that it has good performance in the next 15, 30 and 60 minutes, and the results are better than the existing baselines. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University: Engineering Science is the property of Editorial Office of Journal of Zhengzhou University: Engineering Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Granger causality existed between traffic at different access points of large-scale wireless LANs was discovered.The Granger causality illustrates that the historical traffic of access points that exist causality within target access points help predict the future of target access points with better accuracy than when considering information from the past of target access point alone.Bayesian network to model the causal relationship between access points and adopted a Gaussian mixture model（GMM）was used，as well as a weighted combination of several normal distribution functions in order to approximate the joint probability distribution in Bayesian networks.Finally，the traffic data in large-scale wireless LANs was imported，having hundreds of access points，to verify the accuracy of the proposed method，and a processing flow of analysis，modeling and prediction of traffic flow data was established.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The volatility and self-similarity features of network traffic poses great cha lenge to network traffic prediction. For this purpose, a novel network traffic prediction scheme based on FARIMA-GARCH model was formulated. A novel method was used to get a zero-mean traffic series by a piecewise two-way CUSUM detection algorithm. Then the fraction difference order was evaluated with precision by the presented bounded search method. After obtaining the model para-meters, the prediction algorithm was conducted by using FARIMA-GARCH model. Compared with the traditional me-thod, the limited search method reduces the evaluated error despite a slight computational cost. Then simulation was car-ried out to verify the accuracy of proposed algorithm with real network traffic. The proposed prediction method keeps the same time complexity with the FARIMA model prediction method, and the simulation result shows that the root mean-square error and relative root mean-square error, which closely resemble the RBF prediction method, is less than FARIMA model prediction method. And the interval estimation and volatility prediction performance is excellent.

An advanced network traffic prediction method based on wavelet transformation and FARIMA model was put forward.In the method,the original data was decomposed and reconstructed with Mallat algorithm,then FARIMA model was applied on each branch to predict and integrate the traffic.Simulation is carried out to verify the accuracy of pro-posed algorithm with real network traffic.Compared with the traditional method,the proposed method reduces the pre-diction error.

To improve the performance of tactical data link (TDL) system, This paper proposed a new slot allocation algorithm called TPHSAA. This method added dynamic slot reservation (DSR) mechanism to the traditional static slot assignment, while priorities were taken into consideration. In the DSR part, each node employed Kalman prediction method according to its traffic model to compute the slot number that it would need in the next frame stage, then, sent the value to the NTR which executed the slots assignment algorithm by summarizing all nodes' requirements and priority levels. The simulation results by OP-NET show that TPHSAA performs better in terms of slot utilization, throughput and end to end delay, rational allocation of network bandwidth resources is realized. [ABSTRACT FROM AUTHOR]

Aiming at problems of the existing traffic shaping algorithms in applications of sensor network, a novel traffic shaping algorithm by variable weight combination forecast based is proposed. The reasons of sensor network traffic's sudden randomness and time-varying unequilibrium are analyzed. By modeling the network traffic of fuzziness, randomness and time-varying, based on variable weight combination forecast a Traffic Shaping Algorithm with Variable weight combination forecast is put forward. To get more accurate forecast of traffic value, by optimal combination of two prediction method, Fuzzy AR and Kalman, traffic shaping algorithm is plan ahead shaping rate in order to smooth the output packet flow. Experiments show that the traffic shaping algorithm we proposed can predict the sensor network flow precisely, reduce the packet dropped rate and increase network throughput at the same time, so it can improves the sensor networks' information transmission performance of QOS. [ABSTRACT FROM AUTHOR]

A novel dynamic bandwidth management mechanism based on the traffic prediction is presented to solve the problem of long delay caused by traditional demand assigned multiple access (DAMA). Firstly, the satellite terminals predict the queue length of the buffer, then send the reports to the network control center (NCC). Finally, the resources are allocated to the terminals by NCC. The algorithm performance is validated through the simulation for different types of traffic and different intervals between two consecutive prediction reports. The priority of real time traffic is considered. The delays of DAMA based on prediction and normal case are compared. The theoretical analysis and simulation results show that the new DAMA algorithm can efficiently tackle the problem of delay between bandwidth request and bandwidth assignment. [ABSTRACT FROM AUTHOR]