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首页> 外文学位 >A Compressed Sensing Approach To Channel Estimation For Impulse-Radio Ultra-Wideband (IR-UWB) Communication.
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A Compressed Sensing Approach To Channel Estimation For Impulse-Radio Ultra-Wideband (IR-UWB) Communication.

机译：一种用于脉冲无线电超宽带（IR-UWB）通信的信道估计的压缩传感方法。

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摘要

The thesis addresses the problem of channel estimation in Impluse-Radio Ultra-Wideband (IR-UWB) communication system. The IR-UWB communications utilize low duty cycle pulses to transmit data over the wireless channel. The transmitted energy is distributed over a large number of multipath components (MPCs). At the receiver, these MPCs need to be estimated accurately to capture sufficient energy for successful communications. In our work, the IEEE 802.15.4a channel model is used where the channel is assumed to be Linear Time Invariant (LTI) and thus the problem of channel estimation becomes the estimation of the sparse channel taps and their delays. Since, the bandwidth of the signal is very large and the Nyquist rate sampling (∼ 16 GHz.) is impractical therefore we estimate the channel taps from the subsampled versions of the received signal profile. The transmitted pulse shape considered is the second derivative of the Gaussian pulse. We decompose the channel estimation problem into two parts: (i) estimation of the channel support, followed by, (ii) estimation of the support co-efficients (channel amplitudes). We exploting the signal sparsity and reduce the search space for the channel support by using three different methods: Genetic Algorithm, Correlation and Compressive Sensing. In the classical estimation approach we develop Low-Complexity Maximum Likelihood (LCML) estimator by leveraging the underlying structure of the problem. In the Bayesian framework, first we estimate the decomposed channel by incorporating the a priori multipath arrival time statistics for three different cases of amplitude statistics, namely (i) non-Gaussian, (ii) non-Gaussian with known second order statistics from the IEEE model, and (iii) Gaussian. Second, we jointly estimate the channel support and co-efficients by developing an Approximate Minimum Mean Square Error Estimator (AMMSE). We leverage the structure to reduce the computational complexity and propose a Low-Complexity MMSE (LCMMSE) channel estimator. The performance of the various methods in terms of the Normalized Root Mean Square Error (NRMSE) in estimation of MPC arrival times and energy capture were compared in the presence of AWGN. The novel low-complexity estimators, namely LCML, AMMSE and LCMMSE, presented in the thesis outperform other conventional UWB channel estimators. Furthermore, the computational complexity is much less as compared to that of Compressive Sensing, ML and MMSE estimators.
机译：本文解决了I-UWB通信系统中的信道估计问题。 IR-UWB通信利用低占空比脉冲在无线信道上传输数据。传输的能量分布在大量的多径组件（MPC）上。在接收器处，需要准确估计这些MPC，以捕获足够的能量来成功进行通信。在我们的工作中，使用IEEE 802.15.4a信道模型，其中假定信道为线性时不变（LTI），因此信道估计的问题就变成了稀疏信道抽头及其延迟的估计。由于信号的带宽非常大，奈奎斯特速率采样（约16 GHz）是不切实际的，因此，我们从接收信号轮廓的二次采样版本中估算信道抽头。所考虑的发射脉冲形状是高斯脉冲的二阶导数。我们将信道估计问题分解为两个部分：（i）估计信道支持，然后，（ii）估计支持系数（信道幅度）。我们通过使用三种不同的方法来探索信号稀疏性并减少用于信道支持的搜索空间：遗传算法，相关性和压缩感测。在经典估计方法中，我们通过利用问题的基本结构来开发低复杂度最大似然（LCML）估计器。在贝叶斯框架中，首先我们通过结合三种不同情况下振幅统计的先验多径到达时间统计来估计分解的信道，即（i）非高斯，（ii）非高斯，具有IEEE已知的二阶统计模型；以及（iii）高斯模型。其次，我们通过开发近似最小均方误差估计器（AMMSE）共同估计信道支持和系数。我们利用该结构来减少计算复杂度，并提出了低复杂度MMSE（LCMMSE）信道估计器。在存在AWGN的情况下，比较了各种方法在估计MPC到达时间和能量捕获方面在归一化均方根误差（NRMSE）方面的性能。本文提出的新颖的低复杂度估计器，即LCML，AMMSE和LCMMSE，优于其他常规的UWB信道估计器。此外，与压缩感测，ML和MMSE估计器相比，计算复杂度要低得多。

著录项

  • 作者

    Ahmed, Syed Faraz.;

  • 作者单位

    King Fahd University of Petroleum and Minerals (Saudi Arabia).;

  • 授予单位 King Fahd University of Petroleum and Minerals (Saudi Arabia).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2011
  • 页码 145 p.
  • 总页数 145
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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