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	1. Traffic Engineering in Segment Routing Network Based on Deep Reinforcement Learning
	Wang Yu-Qi,Zhang Xing
	Electrics, Communication and Autocontrol Technology 24 January 2024
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	Abstract:As a new routing paradigm, segment routing (SR) has gained significant attention in research. It offers various working modes, such as SRv6 TE Policy and SRv6 BE, which can be applied to IPv6 network to enable traffic engineering. However, updating from current IP network to SR network presents challenges of high costs and complex deployment. In this paper, we propose a traffic engineering algorithm called PUSR. We utilize a deep reinforcement learning algorithm within PUSR to reduce the maximum link utilization(MLU). The algorithm is based on routing node update and link weight setting. Simulation experiments are conducted to compare traditional routing protocols in two commonly used different topologies. The results demonstrate that PUSR achieves great performance and approaches the theoretical optimum more closely.
	TO cite this article:Wang Yu-Qi,Zhang Xing. Traffic Engineering in Segment Routing Network Based on Deep Reinforcement Learning[OL].[24 January 2024] http://en.paper.edu.cn/en_releasepaper/content/4761952


	2. FGLST: Reducing Latency and System Cost for VR Live Streaming in Edge Networks
	Yuanlin Hu,Xing Zhang
	Electrics, Communication and Autocontrol Technology 29 December 2023
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	Abstract:To reduce the latency and system resource cost of VR live streaming transcoding, the paradigm of edge computing is usually applied to enhance computing speed and reduce the latency, while reducing the bandwidth pressure of the wide area network (WAN). In order to fully utilize the computing power of edge nodes, the task of VR live streaming transcoding needs to be scheduled to each edge node to assist transcoding. However, the current stream scheduling has the problem that the scheduling granularity of VR live streaming on edge nodes is too large. to address this problem, we proposes a fine-grained VR live streaming transcoding (FGLST) architecture. Although this architecture can reduce the scheduling granularity, increase parallelism, and reduce latency, it will generate a large number of tasks that need to be scheduled. Therefore, we further proposes a multi-task scheduling (MTS) algorithm based on the neural network, which introduces a temporary system state vector to solve the problem of not being able to obtain the system state in real time. Finally, we conduct simulation analysis and build a prototype system to evaluate the performance of this architecture, and the result shows that this architecture is superior to the SRS-based VR live streaming transcoding architecture, and this algorithm outperforms the MAB and the RANDOM algorithm.
	TO cite this article:Yuanlin Hu,Xing Zhang. FGLST: Reducing Latency and System Cost for VR Live Streaming in Edge Networks[OL].[29 December 2023] http://en.paper.edu.cn/en_releasepaper/content/4761836

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	3. A Spectrum Allocation Method for Aerospace TT&C Network Based on Automatic Machine Learning
	DAI Guangcai,ZHANG Luyong
	Electrics, Communication and Autocontrol Technology 15 May 2023
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	Abstract:This paper proposes a spectrum allocation method based on automatic machine learning algorithms. The automatic machine learning algorithm based on Bayesian optimization search strategy has shown good performance in spectrum recognition. Based on its characteristics, this paper applies it to predict network traffic and thus completes the task of spectrum allocation for the communication model of the aerospace TT&C network. The experimental results show that applying automatic machine learning algorithms to spectrum allocation, although the main indicators such as network resource utilization rate and network bandwidth blocking rate are slightly inferior to the adaptive path idle degree algorithm with good recognition performance and manual parameter tuning, it still has strong practicality given that it can avoid the tedious and trivial manual parameter tuning work.
	TO cite this article:DAI Guangcai,ZHANG Luyong. A Spectrum Allocation Method for Aerospace TT&C Network Based on Automatic Machine Learning[OL].[15 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760796


	4. Energy-Efficient Data Collection Scheme Based on Spatial Correlation in Biological Detection
	Jiawei Wu,Xi Li
	Electrics, Communication and Autocontrol Technology 08 April 2022
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	Abstract:The development of beyond fifth generation network drives the implement of Internet of Things(IoT) technology in different industries, but it also puts forward complex requirements for solutions in different scenarios. In the biological detection, the requirements about ecological protection and validity of biological data bring challenges to the energy efficiency management of IoT devices. In this paper, the spatial correlation of data collected by adjacent IoT devices and the difference in sensing data volume is considered. An energy-efficient scheme using Unmanned aerial vehicle(UAV) is proposed to compress and collect data. The data aggregation decision among IoT devices and trajectory of UAV is optimized to minimize the energy consumption of IoT devices system with the limitation of UAV battery. Markov Clustering(MCL) algorithm is introduced to solve the problem of digraph clustering and a heuristic algorithm is proposed to optimize the UAV trajectory with little energy loss of devices. Simulation results show that the proposed scheme can reduce the energy consumption of IoT devices.
	TO cite this article:Jiawei Wu,Xi Li. Energy-Efficient Data Collection Scheme Based on Spatial Correlation in Biological Detection[OL].[ 8 April 2022] http://en.paper.edu.cn/en_releasepaper/content/4757336


	5. Edge Offloading Strategy for Mobile Devices in Intelligent Factories Based on Energy Consumption and Delay
	XIAO Ye,ZHOU Xiaoming,LI Zhao,ZHAO Chenglin,XU Fangmin
	Electrics, Communication and Autocontrol Technology 29 March 2021
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	Abstract:To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading method of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on genetic algorithm and particle swarm optimization is used to design the optimal decision task flow offloading strategy which can obtain the minimum energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices.
	TO cite this article:XIAO Ye,ZHOU Xiaoming,LI Zhao, et al. Edge Offloading Strategy for Mobile Devices in Intelligent Factories Based on Energy Consumption and Delay[OL].[29 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754387


	6. Cooperative Task Processing in Fog Computing Networks for Internet of Things
	LIU Xu,LIU Xu,XIE Ren-Chao
	Electrics, Communication and Autocontrol Technology 02 February 2021
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	Abstract:Fog computing is a promising architecture to provide economic and low latency data services for Internet of things (IoT) network systems. In fog computing, multiple mobile devices (MDs) with redundant resources at the edge of the network, commonly referred to as the fog nodes, can be available to help the IoT MD to execute its tasks. In this paper, we design a fog computing framework to enable cooperative processing for delay-sensitive IoT tasks on resource-abundant fog nodes. Based on this framework, multiple fog nodes can cooperate in a fog group and simultaneously process the IoT tasks, so that the computational efficiency of fog networks can be further improved and the resources of fog nodes can be fully utilized. We formulate the cooperative task processing problem as a Winner Determination Problem (WDP) with the objective of maximizing the completion delay reduction of all IoT MDs and propose a cooperative greedy computing algorithm with a low complexity as our solution. Numerical studies confirm that the proposed scheme can obtain a better performance in task completion delay minimization compared with the reference schemes.
	TO cite this article:LIU Xu,LIU Xu,XIE Ren-Chao. Cooperative Task Processing in Fog Computing Networks for Internet of Things[OL].[ 2 February 2021] http://en.paper.edu.cn/en_releasepaper/content/4753566


	7. A Modified Sub-cluster Head Stable Clustering Algorithm for Highly Dynamic Unmanned Aerial Vehicles
	Guo Yuchen,Zhang Xu
	Electrics, Communication and Autocontrol Technology 08 March 2020
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	Abstract:In the UAV network, high-speed movement of nodes will cause frequent network topology updates, making network management more complex and increasing network maintenance overhead. Clustering can increase network capacity, achieve reuse of space resources, and reduce energy consumption overhead. The existing clustering algorithms are proposed for fixed wireless sensor network, mobile ad hoc network with limited mobility or special application such as vehicle network. It is necessary to continue doing the research that could cope with the rapid changes of the network extension caused by the high-speed movement for the UAV.This paper proposes a modified sub-cluster head stable clustering algorithm (SHSC) for highly dynamic unmanned aerial vehicles, which introduces an improved maximum speed similarity parameter when electing a cluster head, so that nodes with similar motion trends are formed the same cluster. The sub-cluster head node is added in each cluster, and becomes a new cluster head immediately when the original cluster head fails or the energy is too low. It is benefit for enhancing the stability of the network. By simulating verification, SHSC is 27% higher than weighted clustering algorithm (WCA) in terms of energy consumption, 22% higher than improved weighted clustering algorithm (IWCA), and 5% higher than optimized stable clustering algorithm (OSCA). In terms of network stability, it is 26% higher than WCA, 22% higher than IWCA, and 19% higher than OSCA.
	TO cite this article:Guo Yuchen,Zhang Xu. A Modified Sub-cluster Head Stable Clustering Algorithm for Highly Dynamic Unmanned Aerial Vehicles[OL].[ 8 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751103


	8. Accurate ZigBee Wireless Sensor Indoor Localization System Based on PSO Algorithm
	ZHENG Ming,LIU Ming-Xin,LI Bei-Ming,XUE Wei
	Electrics, Communication and Autocontrol Technology 06 September 2019
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	Abstract:This paper targets to estimate the distance between the blind node whose position is ambiguous in wireless sensor network and the anchor node whose position is known.And then,estimate location of the blind node according to the geometric relationship among nodes in indoor environments. an approach was used to estimate such a distance. The channel propagation model in indoor environments accord with log-normal shadowing model(LNSM),approach used PSO(Particle Swarm Optimization)) algorithm to improve LNSM model and estimates the distance according to improved LNSM and received signal strength indicator(RSSI).Then constructed a equation Based on the geometric relationship among nodes,solved the equation used Swarm intelligence algorithm,got best estimate of blind node' position. the approach achieved a mean absolute error less than 0.5m in a 10×10 Square test site.
	TO cite this article:ZHENG Ming,LIU Ming-Xin,LI Bei-Ming, et al. Accurate ZigBee Wireless Sensor Indoor Localization System Based on PSO Algorithm[OL].[ 6 September 2019] http://en.paper.edu.cn/en_releasepaper/content/4749584


	9. On Fixed-point Implementation of Log-MPA for SCMA Signals
	Jiaqi Liu, Gang Wu, Shaoqian Li,Olav Tirkkonen
	Electrics, Communication and Autocontrol Technology 03 December 2015
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	Abstract:In this paper, we present a design framework for fixed-point implementation of the log-domainmessage passing algorithm (Log-MPA) for sparse code multiple access (SCMA) signals,and make a detailed comparative analysis on the complexity of Log-MPA and traditional MPA.We also investigate the impact of the number of massage-passing iterations within the Log-MPAdecoding process.Simulation results demonstrate that Log-MPA achieves favorable performance withlow-complexity hardware implementation as compared to MPA.
	TO cite this article:Jiaqi Liu, Gang Wu, Shaoqian Li, et al. On Fixed-point Implementation of Log-MPA for SCMA Signals[OL].[ 3 December 2015] http://en.paper.edu.cn/en_releasepaper/content/4668407


	10. A Novel Satellite Traffic Flow Prediction Scheme Based on Wavelet Neural Network and Genetic Algorithm
	Li Ning,Wang Pengfei,Han Ke,Deng Zhongliang
	Electrics, Communication and Autocontrol Technology 25 November 2015
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	Abstract:Satellite communications network is a kind of delay tolerant network with characters of intermittent connection, long packet queuing time, complex and uncertain delay time, etc. The call congestion rate of new business is higher in delay tolerant network which leads to the reduction of communication quality and the high error code rate. The existing traffic flow prediction scheme is hard to meet new business and individual requirements. It becomes more and more important to study the most effective traffic prediction scheme so as to increase the satellite bandwidth utilization. In this paper, BP neural network (BPNN) has been used as regression model, and the Genetic Algorithm (GA) has been used to search the weights matrix and thresholds of BP neural network. Then the satellite traffic flow prediction scheme based on wavelet neural network and genetic algorithm (WNNGA) was put forward. Simulation results with the real traffic traces show that the proposed method can more accurately predict the future of the satellite network traffic and decrease the processing time compared with previous model method.?????
	TO cite this article:Li Ning,Wang Pengfei,Han Ke, et al. A Novel Satellite Traffic Flow Prediction Scheme Based on Wavelet Neural Network and Genetic Algorithm[OL].[25 November 2015] http://en.paper.edu.cn/en_releasepaper/content/4664049

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