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
信道预测是支撑变电站等电力物联网通信系统自适应传输的重要技术。为了解决过期信道状态信息降低通信系统自适应传输性能的问题,提出了一种基于自适应跳跃学习网络的信道状态信息预测方法。该方法主要包括递归微调算法和混合惩戒网络...信道预测是支撑变电站等电力物联网通信系统自适应传输的重要技术。为了解决过期信道状态信息降低通信系统自适应传输性能的问题,提出了一种基于自适应跳跃学习网络的信道状态信息预测方法。该方法主要包括递归微调算法和混合惩戒网络两部分。其中,前者主要用于微调学习网络的随机输入权重矩阵,后者主要通过两层惩戒网络来解决输出权重矩阵的病态解问题。由于具有oracle属性,自适应跳跃学习网络不仅具有良好的泛化能力,还可以生成稀疏性输出权重矩阵。仿真结果表明,自适应跳跃学习网络在IEEE802.11ah协议的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)通信系统中具有良好的单步预测性能和多步预测性能。展开更多
针对低功耗物联网IEEE802.11ah接入终端周期性上传数据的业务特点,提出一种能量最优的实时限制接入窗口(RAW,restricted access window)分组参数设置(RTRS,real-time RAW setting)算法。在RTRS算法中,多个节点向一个基站接入点(AP,acces...针对低功耗物联网IEEE802.11ah接入终端周期性上传数据的业务特点,提出一种能量最优的实时限制接入窗口(RAW,restricted access window)分组参数设置(RTRS,real-time RAW setting)算法。在RTRS算法中,多个节点向一个基站接入点(AP,accesspoint)报告数据,上行信道资源按照时间被划分成多个Beacon周期。在一个Beacon周期内,AP根据当前周期终端的上传时间预测下一次Beacon周期内终端的上传时间和需要上传数据的终端总量;AP根据一个周期内的接入终端总量计算能量效率,设计能量最优的RAW参数,并通知接入终端按照最优参数接入。仿真结果表明,该方法能够根据上个周期内终端的上传时间准确预测当前网络状态,根据预测的状态动态调整RAW配置参数,显著提高能量效率。展开更多
A 1.4-2 GHz phase-locked loop （PLL） ∑-△ fraction-N frequency synthesizer with automatic fre- quency control （AFC） for 802.1 lah applications is presented. A class-C voltage control oscillator （VCO） ranging fr...A 1.4-2 GHz phase-locked loop （PLL） ∑-△ fraction-N frequency synthesizer with automatic fre- quency control （AFC） for 802.1 lah applications is presented. A class-C voltage control oscillator （VCO） ranging from 1.4 to 2 GHz is integrated on-chip to save power for the sub-GHz band. A novel AFC algorithm is introduced to maintain the VCO oscillation at the start-up and automatically search for the appropriate control word of the switched-capacitor array to extend the PLL tuning range. A 20-bit third-order ∑-△ modulator is utilized to reduce the fraction spurs while achieving a frequency resolution that is lower than 30 Hz. The measurement results show that the frequency synthesizer has achieved a phase noise of 〈 -120 dBc/Hz at 1 MHz offset and consumes 11.1 mW from a 1.7 V supply. Moreover, compared with the traditional class-A counterparts, the phase noise in class-C mode has been improved by 5 dB under the same power consumption.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.
文摘信道预测是支撑变电站等电力物联网通信系统自适应传输的重要技术。为了解决过期信道状态信息降低通信系统自适应传输性能的问题,提出了一种基于自适应跳跃学习网络的信道状态信息预测方法。该方法主要包括递归微调算法和混合惩戒网络两部分。其中,前者主要用于微调学习网络的随机输入权重矩阵,后者主要通过两层惩戒网络来解决输出权重矩阵的病态解问题。由于具有oracle属性,自适应跳跃学习网络不仅具有良好的泛化能力,还可以生成稀疏性输出权重矩阵。仿真结果表明,自适应跳跃学习网络在IEEE802.11ah协议的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)通信系统中具有良好的单步预测性能和多步预测性能。
文摘针对低功耗物联网IEEE802.11ah接入终端周期性上传数据的业务特点,提出一种能量最优的实时限制接入窗口(RAW,restricted access window)分组参数设置(RTRS,real-time RAW setting)算法。在RTRS算法中,多个节点向一个基站接入点(AP,accesspoint)报告数据,上行信道资源按照时间被划分成多个Beacon周期。在一个Beacon周期内,AP根据当前周期终端的上传时间预测下一次Beacon周期内终端的上传时间和需要上传数据的终端总量;AP根据一个周期内的接入终端总量计算能量效率,设计能量最优的RAW参数,并通知接入终端按照最优参数接入。仿真结果表明,该方法能够根据上个周期内终端的上传时间准确预测当前网络状态,根据预测的状态动态调整RAW配置参数,显著提高能量效率。
文摘A 1.4-2 GHz phase-locked loop （PLL） ∑-△ fraction-N frequency synthesizer with automatic fre- quency control （AFC） for 802.1 lah applications is presented. A class-C voltage control oscillator （VCO） ranging from 1.4 to 2 GHz is integrated on-chip to save power for the sub-GHz band. A novel AFC algorithm is introduced to maintain the VCO oscillation at the start-up and automatically search for the appropriate control word of the switched-capacitor array to extend the PLL tuning range. A 20-bit third-order ∑-△ modulator is utilized to reduce the fraction spurs while achieving a frequency resolution that is lower than 30 Hz. The measurement results show that the frequency synthesizer has achieved a phase noise of 〈 -120 dBc/Hz at 1 MHz offset and consumes 11.1 mW from a 1.7 V supply. Moreover, compared with the traditional class-A counterparts, the phase noise in class-C mode has been improved by 5 dB under the same power consumption.
