IEEE 802.11ah is a new Wi-Fi standard for sub-1Ghz communications,aiming to address the challenges of the Internet of Things(IoT).Significant changes in the legacy 802.11 standards have been proposed to improve the ne...IEEE 802.11ah is a new Wi-Fi standard for sub-1Ghz communications,aiming to address the challenges of the Internet of Things(IoT).Significant changes in the legacy 802.11 standards have been proposed to improve the network performance in high contention scenarios,the most important of which is the Restricted Access Window(RAW)mechanism.This mechanism promises to increase the throughput and energy efficiency by dividing stations into different groups.Under this scheme,only the stations belonging to the same group may access the channel,which reduces the collision probability in dense scenarios.However,the standard does not define the RAW grouping strategy.In this paper,we develop a new mathematical model based on the renewal theory,which allows for tracking the number of transmissions within the limited RAW slot contention period defined by the standard.We then analyze and evaluate the performance of RAW mechanism.We also introduce a grouping scheme to organize the stations and channel access time into different groups within the RAW.Furthermore,we propose an algorithm to derive the RAW configuration parameters of a throughput maximizing grouping scheme.We additionally explore the impact of channel errors on the contention within the time-limited RAW slot and the overall RAW optimal configuration.The presented analytical framework can be applied to many other Wi-Fi standards that integrate periodic channel reservations.Extensive simulations using the MATLAB software validate the analytical model and prove the effectiveness of the proposed RAW configuration scheme.展开更多
802.11无线局域网技术的广泛普及,给无线室内定位系统带来了良好的发展契机.提出了一种基于支持向量回归的802.11无线室内定位方法.该方法主要包括离线训练和在线定位两个阶段.离线阶段的主要工作是得到精确的位置预测模型;在线阶段的...802.11无线局域网技术的广泛普及,给无线室内定位系统带来了良好的发展契机.提出了一种基于支持向量回归的802.11无线室内定位方法.该方法主要包括离线训练和在线定位两个阶段.离线阶段的主要工作是得到精确的位置预测模型;在线阶段的主要工作是根据移动设备的接收信号强度(received signal strength,简称RSS)进行在线定位.由于存在室内环境复杂、信道拥塞、障碍物影响和节点的通信半径有限等问题,移动设备的接收信号强度易受干扰,复杂多变.针对以上问题,离线阶段对接收信号强度信息进行统计分析,得出数据过滤规则,对训练数据集进行过滤,以此提高训练样本质量,从而提高支持向量回归预测模型的质量.在线阶段使用连续K次测量定位法获取信号强度信息,保证训练样本与在线输入信息之间的一致性,提高最终的定位精度.通过实验对该定位方法进行了综合对比分析,实验结果表明:与常用概率定位法、神经网络法相比,该方法具有更高的定位精度,同时具有对移动设备的存储容量及其计算能力要求较低的特点.展开更多
基金supported by the Spanish Ministry of Science,Education and Universities,the European Regional Development Fund and the State Research Agency,Grant No.RTI2018-098156-B-C52.
文摘IEEE 802.11ah is a new Wi-Fi standard for sub-1Ghz communications,aiming to address the challenges of the Internet of Things(IoT).Significant changes in the legacy 802.11 standards have been proposed to improve the network performance in high contention scenarios,the most important of which is the Restricted Access Window(RAW)mechanism.This mechanism promises to increase the throughput and energy efficiency by dividing stations into different groups.Under this scheme,only the stations belonging to the same group may access the channel,which reduces the collision probability in dense scenarios.However,the standard does not define the RAW grouping strategy.In this paper,we develop a new mathematical model based on the renewal theory,which allows for tracking the number of transmissions within the limited RAW slot contention period defined by the standard.We then analyze and evaluate the performance of RAW mechanism.We also introduce a grouping scheme to organize the stations and channel access time into different groups within the RAW.Furthermore,we propose an algorithm to derive the RAW configuration parameters of a throughput maximizing grouping scheme.We additionally explore the impact of channel errors on the contention within the time-limited RAW slot and the overall RAW optimal configuration.The presented analytical framework can be applied to many other Wi-Fi standards that integrate periodic channel reservations.Extensive simulations using the MATLAB software validate the analytical model and prove the effectiveness of the proposed RAW configuration scheme.
文摘802.11无线局域网技术的广泛普及,给无线室内定位系统带来了良好的发展契机.提出了一种基于支持向量回归的802.11无线室内定位方法.该方法主要包括离线训练和在线定位两个阶段.离线阶段的主要工作是得到精确的位置预测模型;在线阶段的主要工作是根据移动设备的接收信号强度(received signal strength,简称RSS)进行在线定位.由于存在室内环境复杂、信道拥塞、障碍物影响和节点的通信半径有限等问题,移动设备的接收信号强度易受干扰,复杂多变.针对以上问题,离线阶段对接收信号强度信息进行统计分析,得出数据过滤规则,对训练数据集进行过滤,以此提高训练样本质量,从而提高支持向量回归预测模型的质量.在线阶段使用连续K次测量定位法获取信号强度信息,保证训练样本与在线输入信息之间的一致性,提高最终的定位精度.通过实验对该定位方法进行了综合对比分析,实验结果表明:与常用概率定位法、神经网络法相比,该方法具有更高的定位精度,同时具有对移动设备的存储容量及其计算能力要求较低的特点.
