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Subspace-Based Blind Channel Estimation and Tracking for MIMO-OFDM Systems.

机译：MIMO-OFDM系统中基于子空间的盲信道估计和跟踪。

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摘要

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) is now widely considered as a favored technology for emerging and future generation wireless systems. MIMO-OFDM aims to achieve increased channel capacity limit by exploiting the use of multiple antennas in combination with multi-carrier orthogonal modulation. While the possibility of achieving this limit is bestowed on the invention of capacity-achieving coding and decoding techniques, in reality, this prospect relies heavily on the existence and use of advanced channel estimation techniques. To facilitate fast and reliable channel estimation in MIMO-OFDM systems, pilot symbol insertion is usually considered; however, the channel capacity is greatly reduced by inserting those pilot symbols. Therefore, employing fast-converging and reliable blind channel estimation for MIMO-OFDM seems to be an attractive solution for future wireless systems.;In this thesis, we propose a new subspace-based blind channel estimator that requires only a comparably short time averaging period. We consider the design of such an estimator directly in the frequency domain, as opposed to the majority of existing designs in which estimators are developed in the time domain. Our first contribution is to propose and investigate a novel subspace-based estimator with reduced time averaging, by exploiting the frequency correlation among adjacent subcarriers, residing within the coherence bandwidth of the broadband channels in typical MIMO-OFDM scenarios. To reduce the high computational complexity incurred by the eigenvalue decomposition and the associated ambiguity matrix, our second contribution is to develop an improved, adaptive version of the estimator for enhancing its capability under MIMO time-varying conditions. This is achieved by employing a modified form of the orthogonal iteration for efficient subspace tracking along with a precoding technique that allows a reduction in the size of ambiguity matrix. Numerical experiments demonstrate that the proposed techniques can indeed outperform several benchmark estimators in various practical scenarios.;To this end, blind channel estimation based on second order statistics (SOS), instead of higher order statistics (HOS), has been widely considered as a suitable candidate. Amid SOS-based blind approaches, subspace-based estimation is attractive since reliable estimates can often be obtained in a simple form by optimizing a quadratic cost function. Nonetheless, the performance of the subspace-based blind channel estimators may still be seriously degraded under time-varying conditions. This problem can generally make overall performance unsatisfactory, especially in MIMO-OFDM systems whose number of subcarriers is large. In order to overcome this limitation and successfully employ subspace-based channel estimation in MIMO-OFDM systems, it is essential to minimize the required length of the underlying time averaging period.
机译：现在，多输入多输出（MIMO）正交频分复用（OFDM）被广泛认为是新兴和未来无线系统的首选技术。 MIMO-OFDM旨在通过结合使用多天线和多载波正交调制来实现增加的信道容量限制。尽管实现此限制的可能性是通过实现容量的编码和解码技术的发明而实现的，但实际上，这种前景很大程度上取决于高级信道估计技术的存在和使用。为了在MIMO-OFDM系统中促进快速可靠的信道估计，通常考虑使用导频符号插入。然而，通过插入那些导频符号，信道容量大大降低。因此，对MIMO-OFDM采用快速收敛且可靠的盲信道估计似乎是未来无线系统的一种有吸引力的解决方案。本文提出了一种新的基于子空间的盲信道估计器，该估计器仅需要较短的时间平均周期。我们直接在频域中考虑这种估计器的设计，这与大多数在时域中开发估计器的现有设计相反。我们的第一个贡献是通过利用相邻子载波之间的频率相关性（通常在MIMO-OFDM场景中位于宽带信道的相干带宽内），提出并研究一种新颖的，基于子空间的估计器，该估计器具有减少的时间平均。为了减少特征值分解和相关的歧义矩阵引起的高计算复杂性，我们的第二个贡献是开发了一种改进的自适应版本的估计器，以增强其在MIMO时变条件下的能力。这是通过采用改进形式的正交迭代进行高效子空间跟踪以及允许减小歧义矩阵大小的预编码技术来实现的。数值实验表明，所提出的技术确实可以在各种实际情况下优于几种基准估计器。为此，基于二阶统计量（SOS）而非高阶统计量（HOS）的盲信道估计已被广泛认为是一种合适的候选人。在基于SOS的盲法中，基于子空间的估计很有吸引力，因为可靠的估计通常可以通过优化二次成本函数以简单的形式获得。但是，在时变条件下，基于子空间的盲信道估计器的性能可能仍会严重降低。这个问题通常会使整体性能不令人满意，尤其是在子载波数量很大的MIMO-OFDM系统中。为了克服此限制并成功地在MIMO-OFDM系统中采用基于子空间的信道估计，必须将基础时间平均周期的所需长度最小化。

著录项

  • 作者

    Tu, Chao-Cheng.;

  • 作者单位

    McGill University (Canada).;

  • 授予单位 McGill University (Canada).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 172 p.
  • 总页数 172
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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