软件定义思想在无线领域的应用促使了软件定义无线网络(software defined wireless networking,SDWN)的兴起,SDWN分离无线控制平面及数据平面,开放无线网络可编程接口,简化了网络管理,是第五代移动通信网络(5G)的重要发展方向之一。从...软件定义思想在无线领域的应用促使了软件定义无线网络(software defined wireless networking,SDWN)的兴起,SDWN分离无线控制平面及数据平面,开放无线网络可编程接口,简化了网络管理,是第五代移动通信网络(5G)的重要发展方向之一。从软件定义无线网络诞生发展入手,总结了SDWN的网络体系结构;阐述了主要关键技术的研究进展,包括移动管理、密集部署、负载均衡等;讨论了SDWN在不同场景下的应用部署,并对未来研究工作所面临的挑战及发展趋势进行了分析。展开更多
For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be colle...For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.展开更多
文摘软件定义思想在无线领域的应用促使了软件定义无线网络(software defined wireless networking,SDWN)的兴起,SDWN分离无线控制平面及数据平面,开放无线网络可编程接口,简化了网络管理,是第五代移动通信网络(5G)的重要发展方向之一。从软件定义无线网络诞生发展入手,总结了SDWN的网络体系结构;阐述了主要关键技术的研究进展,包括移动管理、密集部署、负载均衡等;讨论了SDWN在不同场景下的应用部署,并对未来研究工作所面临的挑战及发展趋势进行了分析。
基金Sponsored by the National Natural Science Foundation of China(Grant No.61101122)the National High Technology Research and Development Program of China(Grant No.2012AA120802)the National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant No.2012ZX03004-003)
文摘For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.
