802．11WLAN中一种基于竞争窗口的分组调度算法

IEE—E802．11MAC层中的分布式协调功能DCF（distributed coordination function）使用随机退避机制来解决信道竞争问题，导致信道资源不能充分利用，特别是在高负载的网络系统中，信道带宽在碰撞状态下浪费严重．本文提出了基于竞争窗口的分组调度算法，通过增加一个竞争窗口将节点间的竞争划分为两个阶段进行，其中只有通过第一退避阶段的节点才能进入下一个退避阶段，完成第二退避阶段的节点才能开始访问信道．根据具体网络情况，选择合适的第二阶段的最小窗口值，得到相应的网络性能．仿真结果表明该算法在高负载的网络中能够提高信道带宽利用率．     

A random adaptive method to adjust MAC parameters in IEEE802.11e WLAN

The IEEE 802.11e standard is proposed to provide QoS support in WLAN by providing prioritized differentiation of traffic. Since all the stations in the same priority access category (AC) have the same set of parameters, when the number of stations increases, the probability of different stations in the same AC choosing the same values will increase, which will result in collisions. Random adaptive MAC (medium access control) parameters scheme (RAMPS) is proposed, which uses random adaptive MAC differentiation parameters instead of the static ones used in the 802.11e standard. The performance of RAMPS is compared with that of enhanced distributed coordination access (EDCA) using NS2. The results show that RAMPS can reduce collision rate of the AC and improve the throughput by using adaptive random contention window size and inter-frame spacing values. RAMPS ensures that at any given time, several flows of the same priority have different MAC parameter values. By using the random offset for the inter-frame spacing value and the backoff time, RAMPS can provide intra-AC differentiation. The simulation results show that RAMPS outperforms EDCA in terms of both throughput and end-to-end delay irrespective of the traffic load. Foundation item: Project(60673164) supported by the National Natural Science Foundation of China; Project(06JJ10009) supported by the Natural Science Foundation of Hunan Province, China; Project(20060533057) supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China; Project(2008CB317107) supported by the Major State Basic Research and Development Program of China; Project(NCET-05-0683) supported by the Program for New Century Excellent Talents in University     

MAC layer approaches to mitigating the spectrum underutilization due to the overlapping BSS problem in WLAN

Considering the OBSS-induced spectrum underutilization problem in Wireless Local Area Networks(WLAN), two complementary contention-based Media Access Control(MAC) layer schemes are proposed by setting up a virtual-primary channel for contention. The proposed transmission opportunity(TXOP) scheme is designed for transmitting long data packets, which is suitable for applications requiring high throughputs. In contrast, the multi-contention(MC) scheme is designed for transmitting short data packets, which is suitable for applications requiring short delays. By efficiently exploiting the OBSS-induced underutilized spectrum, simulation results verify the fact that the proposed TXOP scheme can increase the throughput greatly and the proposed MC scheme can reduce the delay for short data packets significantly.     

IEEE802.11中的一种新型信道竞争退避算法

为了提高分布式协调功能(DCF)的性能,提出了计算暂停次数退避算法(SCB).该算法采用指数加权移动平均(EWMA)对其平滑,建立竞争窗口值动态相关性,实现了合理分配、规划信道带宽.仿真结果表明,SCB算法在实际吞吐量、公平性、碰撞速率上优于二进制指数退避算法(BEB)、指数递增指数递减退避算法(EIED)、自适应增强型分布式协调功能算法(AEDCF)     

基于IEEE802.11g标准的WLAN性能分析与测试

分析了IEEE802．11g标准，介绍了无线局域网的拓扑结构，构建了一个基于802．11g标准的WLAN。采用网络传输率测试软件Qcheck，通过无线到有线网络客户端、无线到无线客户端、混合网络模式的TCP传输性能和穿透性能4个方面的测试，对基于802．11g标准的WLAN性能进行了实验验证。通过与802.11b标准的性能对比，说明其优越性。     

一种无线传感器网络路由协议LEACH的改进算法

针对低功耗自适应集簇分层型协议LEACH(low energy adaptive clustering hierarchy)的节点生命周期短和能量消耗不平衡的问题,提出了一种LEACH协议的改进算法.算法的主要思想是考虑了节点的当前位置以及当前能量,从而可以使簇头的分布更加均匀,延长节点的生命周期.对改进后的LEACH协议和原LEACH协议进行仿真,结果表明改进后的协议在生存时间上提高了40.7%,并增加了数据的发送量,减少了节点的能量消耗.     

IEEE802.11无线网络中DCF模式媒介服务状态分析

IEEE 80211标准的分布式协调功能（DCF）模式下,节点通过竞争获得无线媒介的访问权,导致媒介服务时间远大于数据帧传输时间. 通过深入分析数据帧传输、数据帧碰撞以及媒介空闲3种媒介状态的转换情况,采用近似二项分布建立媒介状态描述模型,获得了媒介服务时间估计值. 仿真结果表明,所建立的模型能够准确地衡量给定网络状态下的媒介服务时间.     

基于WBAN的生物特征检测重构算法

生物特征检测是无线体域网的关键问题。本文利用信号群的内相关性和互相关性结构,运用分布式压缩感知理论,建立一种新型分布式编码重构算法,即信号群与联合稀疏模型(Joint Sparse Model,JSM-1)。将该模型运用于无线体域网中,提出了联合稀疏信号模型的重构算法,仿真结果验证了该算法的有效性。     

一种集成卷积神经网络和深信网的步态识别与模拟方法

针对高斯过程的条件受限玻尔兹曼机(Gaussian-based conditional restricted Boltzmann machine, GCRBM)时序模型可以对单一种类的步态时序数据进行很好的预测,但对多类步态时序数据难以识别和预测的问题,提出一种集成卷积神经网络(convolutional neural network, CNN)和深信网(deep belief network, DBN)的步态识别与模拟方法。利用所有类步态数据训练多个不同结构的CNNs模型,利用多类数据训练多个DBNs模型学习低维特征,并通过低维特征训练多个GCRBMs模型。在步态识别与模拟时,CNNs分类器通过投票法确定步态数据的类别;通过识别到的类所对应的DBNs模型低维特征作为对应GCRBMs模型的输入预测目标数据的后期时序低维特征;利用DBNs重构阶段将后期时序低维特征模拟出步态图像。在CASIA系列步态数据集上的试验结果表明:与支持向量机(support vector machine, SVM)、集成DBN和CNN等方法相比,本研究方法的识别率有一定的提高,提出的模型能够根据步态时序预测结果模拟出真实的步态序列图像,证实了模型的有效性。