针对超高速IEEE 802.11ac网络中速率自适应算法的效率是决定系统性能的关键因素,本文提出了一种基于信息统计的高效速率自适应算法(AMRA)。该算法采用发送和接收相结合的方式精确地估计当前信道状况,并在由发送带宽、空间流数、物理层...针对超高速IEEE 802.11ac网络中速率自适应算法的效率是决定系统性能的关键因素,本文提出了一种基于信息统计的高效速率自适应算法(AMRA)。该算法采用发送和接收相结合的方式精确地估计当前信道状况,并在由发送带宽、空间流数、物理层的调制模式所确定的三维空间内选择最佳的速率。通过实际测试验证,结果表明在不同的信道环境下,该算法的吞吐率性能均优于Atheros MIMO RA、Minstrel等速率自适应算法,有效提高了网络吞吐量性能和利用率。展开更多
目前IEEE802.11设备提供了多个不同的传输速率,对动态变化的无线信道选择不同的传输速率可以使系统性能最大化。目前已经有许多速率自适应算法被提出,但是没有一种算法适用于所有环境下的动态变化无线信道,而不同工作环境下需要选择不...目前IEEE802.11设备提供了多个不同的传输速率,对动态变化的无线信道选择不同的传输速率可以使系统性能最大化。目前已经有许多速率自适应算法被提出,但是没有一种算法适用于所有环境下的动态变化无线信道,而不同工作环境下需要选择不同种类的速率自适应算法。概述了不同种类的速率自适应算法,剖析了每种算法影响其性能提高的因素,并对不同种类的算法进行了对比,最后提出了现有速率自适应算法所存在的问题。此外,用NS-2(Network Simulator version 2)仿真软件验证了RTS/CTS(Request-to-Send/Clear-to-Send)信号的作用,它的启用会降低系统频带利用率,但同时也能有效地消除由多个移动终端共用一个AP(access point)引起的碰撞。展开更多
For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and de...For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.展开更多
With its rapid development in the wireless markets, IEEE 802.11 WLAN is experiencing a huge popularity. However, due to the limitation of frequency bandwidth of WLANs, it is essential that the available radio resource...With its rapid development in the wireless markets, IEEE 802.11 WLAN is experiencing a huge popularity. However, due to the limitation of frequency bandwidth of WLANs, it is essential that the available radio resource should be fully utilized to offer different services to multiple users. In order to maximize system throughput while still guaranteeing the fairness among users, a proportional fairness based algorithm is proposed in this work. Since most of the previous resource allocation algorithms were simply based on the channel conditions without taking into account user's demand, in this paper, we introduce the theory of fuzzy synthetic evaluation(FSE) which also allows us to consider user's demand as an important factor. As such, the fairness among users can be improved based on different users' requirements for services. In addition, a channel state information based rate adaptation scheme is also proposed. Through simulation studies, the results clearly validate that our proposed scheme shows advantages on providing user fairness while still improving the system throughput.展开更多
文摘针对超高速IEEE 802.11ac网络中速率自适应算法的效率是决定系统性能的关键因素,本文提出了一种基于信息统计的高效速率自适应算法(AMRA)。该算法采用发送和接收相结合的方式精确地估计当前信道状况,并在由发送带宽、空间流数、物理层的调制模式所确定的三维空间内选择最佳的速率。通过实际测试验证,结果表明在不同的信道环境下,该算法的吞吐率性能均优于Atheros MIMO RA、Minstrel等速率自适应算法,有效提高了网络吞吐量性能和利用率。
文摘目前IEEE802.11设备提供了多个不同的传输速率,对动态变化的无线信道选择不同的传输速率可以使系统性能最大化。目前已经有许多速率自适应算法被提出,但是没有一种算法适用于所有环境下的动态变化无线信道,而不同工作环境下需要选择不同种类的速率自适应算法。概述了不同种类的速率自适应算法,剖析了每种算法影响其性能提高的因素,并对不同种类的算法进行了对比,最后提出了现有速率自适应算法所存在的问题。此外,用NS-2(Network Simulator version 2)仿真软件验证了RTS/CTS(Request-to-Send/Clear-to-Send)信号的作用,它的启用会降低系统频带利用率,但同时也能有效地消除由多个移动终端共用一个AP(access point)引起的碰撞。
基金Supported by the National Natural Science Foundation of China (60904018, 61203040)the Natural Science Foundation of Fujian Province of China (2009J05147, 2011J01352)+1 种基金the Foundation for Distinguished Young Scholars of Higher Education of Fujian Province of China (JA10004)the Science Research Foundation of Huaqiao University (09BS617)
文摘For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.
基金partially supported by the Academy of Finland (Decision No. 284748, 288473)
文摘With its rapid development in the wireless markets, IEEE 802.11 WLAN is experiencing a huge popularity. However, due to the limitation of frequency bandwidth of WLANs, it is essential that the available radio resource should be fully utilized to offer different services to multiple users. In order to maximize system throughput while still guaranteeing the fairness among users, a proportional fairness based algorithm is proposed in this work. Since most of the previous resource allocation algorithms were simply based on the channel conditions without taking into account user's demand, in this paper, we introduce the theory of fuzzy synthetic evaluation(FSE) which also allows us to consider user's demand as an important factor. As such, the fairness among users can be improved based on different users' requirements for services. In addition, a channel state information based rate adaptation scheme is also proposed. Through simulation studies, the results clearly validate that our proposed scheme shows advantages on providing user fairness while still improving the system throughput.
