Spectrum sensing scheduling for group spectrum sharing in cognitive radio networks

This paper addresses the issues on spectrum sharing in a cognitive radio network consisting of a primary user and a group of cognitive users. Each cognitive user may occupy a non‐overlapped sub‐band of the primary spectrum, but it needs to perform spectrum sensing independently before accessing the sub‐band. To reduce the complexity of spectrum sensing and thus energy consumption, this paper proposes a scheduled spectrum sensing scheme. First, we consider a single spectrum sensing scenario where only one cognitive user is elected to perform spectrum sensing, and then it broadcasts its sensing results to the other cognitive users. The scheduled spectrum sensing scheme works in both network‐centric and user‐centric ways. Next, the scheduled spectrum sensing scheme is further generalized to work in a multiple spectrum sensing scenario. The results show the effectiveness of the proposed schemes compared with the traditional schemes where all cognitive users may perform spectrum sensing at the same time. Copyright © 2010 John Wiley & Sons, Ltd.     

Simulations and experiments for optimal deployment of an RFID‐based location‐aware system

The proliferation of communication and mobile computing devices and local‐area wireless networks has cultivated a growing interest in location‐aware systems and services. An essential problem in location‐aware computing is the determination of physical locations. RFID technologies are gaining much attention, as they are attractive solutions to indoor localization in many healthcare applications. In this paper, we propose a new indoor localization methodology that aims to deploying RFID technologies in achieving accurate location‐aware undertakings with real‐time computation. The proposed algorithm introduces means to improve the accuracy of the received RF signals. Optimal settings for the parameters in terms of reader and reference tag properties were investigated through simulations and experiments. The experimental results indicate that our indoor localization methodology is promising in applications that require fast installation, low cost and high accuracy. Copyright © 2009 John Wiley & Sons, Ltd.     

机器学习在5G超密集网络切换中的应用

超密集网络（Ultra-dense Network,UDN）作为5G网络架构的关键技术,其切换时延及无效切换等已成为网络发展的巨大挑战。提出了一种基于集成策略的机器学习算法,并结合用户的移动性数据,进行较高精确度的切换预测,减少切换时延及非必要切换等目标。仿真结果表明,采用结合改进机器学习算法的切换策略,不必要切换率降低了40.2%,平均时延降低了28.6%。     

Distributed congestion control strategy using network utility maximization theory in VANET

Cooperative vehicle safety system (CVSS) rely on periodical beacons to track neighboring vehicles.High traffic density often causes channel congestion,seriously damaging the performance of CVSS.Existing congestion control strategies aim to ensure the performance in network layer,without considering the service requirements of vehicles in different driving contexts.To solve the problem,a distributed congestion control strategy using network utility maximization (NUM) theory was proposed.First of all,the NUM model for channel resource allocation was introduced.A utility function reflecting vehicle’s safety requirements was proposed in the model.Then under the condition of fixed transmit powers,a optimization problem of channel resource allocation was proposed.Lastly,to solve the optimization problem,a distributed congestion control algorithm named utility-based rate congestion control (UBRCC) algorithm was designed,the algorithm worked out the optimal beaconing rate by updating vehicle’s congestion price,realizing the resource allocation according to vehicle’s safety requirements.Simulation results validate that UBRCC algorithm can efficiently control channel congestion,reduce transmission delay,ensure reliable data transmission and satisfies the requirements of safety applications.     

Multicast service protection algorithm based on elastic optical network

With the rapid growth of the network traffic,the elastic optical network (EON) has been proposed as a promising solution due to its high spectrum efficiency and flexible bandwidth provision.Meanwhile,multicast routing and spectrum allocation,and the survivability of the network become more challenging than that in the conventional optical network.The routing for multicast traffic and its protection algorithm in EON was investigated.An integer linear programming (ILP) formulation with the objective to minimize total spectrum consumption was presented.In addition,a heuristic algorithm called multicast sub-tree protection algorithm (MSPA) to achieve sufficient protection and satisfy resources savings was designed.The simulation results demonstrate that comparing with the traditional multicast routing and protection algorithm,MSPA performs well in improving the blocking probability and the spectrum utilization of the network.     

Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network

The container cloud represented by Docker and Kubernetes has the advantages of less additional resource overhead and shorter start-up and destruction time.However there are still resource management issues such as over-supply and under-supply.In order to allow the Kubernetes cluster to respond “in advance” to the resource usage of the applications deployed on it,and then to schedule and allocate resources in a timely,accurate and dynamic manner based on the predicted value,a cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network was proposed,based on historical data to predict future demand for resources.To find the optimal combination of parameters,the parameters were optimized using TPOT thought.Experiments on the CPU and memory of the Google dataset show that the model has better prediction performance than other models.     

DL-assisted programmable multilayer network application awareness system for IP-over-EON

In order to realize real-time and fine-granularity network monitoring and adjustment,and satisfy the specific QoS demands of various applications,a deep learning (DL) assisted programmable multilayer network application performance awareness system for IP-over-EON was proposed.The distributed network monitoring based on network application performance awareness was combined with centralized network management.The multilayer and fine-grained network monitoring was implemented by distributed network monitoring,and the data analysis through DL was performed.Experimental results indicate that by combining distributed and centralized processing seamlessly,the proposed network monitoring system can not only realize timely and automatic network control and management but also provide superior scalability.     

基于深度卷积神经网络和多核学习的遥感图像分类方法

为解决传统遥感图像分类方法特征提取过程复杂、特征表现力不强等问题,该文提出一种基于深度卷积神经网络和多核学习的高分辨率遥感图像分类方法。首先基于深度卷积神经网络对遥感图像数据集进行训练,学习得到两个全连接层的输出将作为遥感图像的两种高层特征;然后采用多核学习理论训练适合这两种高层特征的核函数,并将它们映射到高维空间,实现两种高层特征在高维空间的自适应融合;最后在多核融合特征的基础上,设计一种基于多核学习-支持向量机的遥感图像分类器,对遥感图像进行精确分类。实验结果表明,与目前已有的基于深度学习的遥感图像分类方法相比,该算法在分类准确率、误分类率和Kappa系数等性能指标上均有所提升,在实验测试集上3个指标分别达到了96.43%, 3.57%和96.25%,取得了令人满意的结果。

     

互联网网络时延特征研究

互联网宏观拓扑结构下时延特征的研究对互联网结构与性能的理解具有重要作用。择取CAIDA Ark项目下分别位于4个不同地区的探测数据,提取网络时延与通信直径,发现网络时延与通信直径关系很小且超过70%的有效路径中存在支配时延。经分析,正是支配时延的存在影响了网络时延与通信直径的关系。对支配时延链路两端进行地理映射,发现支配时延以较高概率出现在同一国家同一城市某一链路传输之间。在此基础上,得出链路两端距离小于5 000 km的支配时延主要由排队时延组成,而链路两端距离大于5 000 km的支配时延主要由传播时延组成的结论。     

网电空间战场样式及致胜机理研究

从分析网电空间战场的基本作战样式和在网电空间面临的严峻形势出发,按照依靠网电空间的作战和网电空间内的作战两个基本分类对网电空间战场的基本样式进行了分类探讨并指出了原有分类方法的局限性;在此基础上,对网电空间战场整体致胜机理进行了初步研究,提出了三点尚不成熟的在网电空间战场中谋求主动权的基本考量,以此为网电空间战场相关人员提供借鉴的思路。