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Parameter estimation in distributed sensor networks and CFO estimation in OFDM systems.

机译：分布式传感器网络中的参数估计和OFDM系统中的CFO估计。

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摘要

The research of this dissertation addresses two important issues relevant to parameter estimation: maximizing network lifetime (NLT) of distributed wireless sensor networks deployed to estimate and track a common parameter; and carrier-frequency-offset (CFO) estimation in orthogonal frequency-division-multiplexing (OFDM) systems. In practice, the sensor nodes (SNs) of most wireless sensor networks are energy-constrained. To estimate and track a common parameter using such energy-constrained networks, we propose to employ set-membership adaptive filter (SMAF) in each of the SNs. The SMAF updates (thus transmits) parameter estimates only if the magnitude of the estimation error exceeds a predefined threshold, which affects the frequency of updates and the overall estimation performance. This approach renders a nicely formulated trade-off mechanism, resulting in more frugal energy use for SNs, and prolonged NLT. This approach also leads to a solution framework that relates maximizing NLT to network performance, i.e., meeting a performance constraint defined based on the mean-squared-deviation (MSD) of the consensus estimate. The NLT maximization is posed as a constrained optimization problem whose solution yields the optimal error thresholds for the SMAFs that maximize the NLT. The optimal solution is obtained by using an iterative binary search algorithm, which also solves a problem of node selection. The robustness of the proposed solution is investigated with respect to the spatial correlation and the impact of the uncertainty in the knowledge of spatial correlation on the NLT. We also solve NLT maximization for the case of optimum energy with a constraint on total energy. Simulation results using data from real-world applications show that the proposed approach offers substantially prolonged NLT over conventional tracking algorithms such as normalized least mean-squares (NLMS) adaptive filters. The second part of the dissertation focuses on estimating CFO in OFDM systems taking into account power amplifiers (PA) nonlinearity in time-varying multipath fading channels, like those in mobile environments. We derive Cramer-Rao lower bound (CRLB) and approximate maximum-likelihood-estimators for CFO estimation in the static and time-varying channel scenarios. Analysis and simulation reveal that Doppler fading introduces a floor on the accuracy of CFO estimation. We then study the impact of PA nonlinearity on the accuracy of CFO estimation and present the modified CRLBs for the same. Performance of an ideal predistortion (PD) scheme is compared to that of a practical PD scheme employing unscented Kalman filter (UKF). Simulation results corroborate our theoretical analysis and prove the efficacy of our proposed PD filter to compensate for nonlinearity.
机译：本论文的研究解决了与参数估计有关的两个重要问题：最大化分布式无线传感器网络的网络寿命（NLT），该分布式无线传感器网络用于估计和跟踪公共参数。正交频分复用（OFDM）系统中的载波频偏（CFO）估计。实际上，大多数无线传感器网络的传感器节点（SN）都是能量受限的。为了使用这样的能量受限网络来估计和跟踪公共参数，我们建议在每个SN中采用集合成员自适应滤波器（SMAF）。仅当估计误差的大小超过预定义的阈值时，SMAF才会更新（因此发送）参数估计，这会影响更新的频率和总体估计性能。这种方法提供了一种精心设计的权衡机制，从而导致SN节约能源的使用更多，并且延长了NLT。该方法还导致一种解决方案框架，该框架将最大化NLT与网络性能相关联，即满足基于共识估计的均方差（MSD）定义的性能约束。 NLT最大化被视为一个约束优化问题，该问题的解决方案为使NLT最大化的SMAF产生了最佳误差阈值。通过使用迭代二进制搜索算法获得最优解，这也解决了节点选择的问题。针对空间相关性以及空间相关性知识中的不确定性对NLT的影响，研究了所提出解决方案的鲁棒性。对于总能量受约束的最佳能量，我们还解决了NLT最大化问题。使用来自实际应用程序的数据进行的仿真结果表明，与常规跟踪算法（例如归一化最小均方（NLMS）自适应滤波器）相比，该方法可提供更长的NLT。论文的第二部分着重于估计OFDM系统中的CFO，其中考虑了时变多径衰落信道中功率放大器（PA）的非线性，例如在移动环境中。我们推导了Cramer-Rao下界（CRLB）和近似最大似然估计器，用于在静态和时变信道场景中进行CFO估计。分析和仿真表明，多普勒衰落为CFO估计的准确性引入了下限。然后，我们研究了PA非线性对CFO估计准确性的影响，并提出了相同的改进的CRLB。将理想预失真（PD）方案的性能与采用无味卡尔曼滤波器（UKF）的实际PD方案的性能进行了比较。仿真结果证实了我们的理论分析，并证明了我们提出的PD滤波器补偿非线性的功效。

著录项

  • 作者

    Malipatil, Amaresh.;

  • 作者单位

    University of Notre Dame.;

  • 授予单位 University of Notre Dame.;
  • 学科 Electrical engineering.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 126 p.
  • 总页数 126
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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