首页> 外文学位 >Coded Compressed Sensing with Applications to Wireless Communication.
【24h】

Coded Compressed Sensing with Applications to Wireless Communication.

机译:编码压缩传感及其在无线通信中的应用。

获取原文
获取原文并翻译 | 示例

摘要

Compressed sensing is a new paradigm that exploits the sparsity of signals to reduce the number of measurements required to recover a representation. This is accomplished using the general notion of inner-products as measurements, encapsulated in "measurement matrices." In this work, we focus on designing both measurement matrices as well as compressed sensing recovery algorithms. We consider several measurement code designs and recovery schemes with applications to particular systems. First, we consider chirp-coded compressed sensing measurements which, with a jointly designed recovery algorithm, is designed for computationally efficient recovery. For M measurements, O( M log M) recovery is possible, a significant speed improvement over conventional random signals and recovery methods. Subsequently, we consider OFDM channel estimation in the context of compressed sensing and note that measurement matrices are restricted to the form of sub-Fourier matrices. We provide a method to manifest suitable matrices for recovering sparse channels by deterministically selecting a few pilot tones. Next, we consider how the compressed sensing paradigm can be used to build novel wireless systems. We design a muliuser detection scheme for random access on asynchronous channels. For this system, we develop new compressed sensing recovery theory and design a codebook suitable for the recovery of sparse sets of active users. Finally, we design a virtual full-duplex adhoc wireless network system using half-duplex hardware. In the system, nodes use codes containing "listening symbols" during which the devices sense the wireless channel. When active nodes in the network transmit simultaneously, each node inherits a unique compressed sensing problem to recover the data from its neighbors.;The work in this thesis shows how, with careful consideration of the application, compressed sensing with coded matrices can provide great performance improvements and novel system designs.
机译:压缩感测是一种新的范例,它利用信号的稀疏性来减少恢复表示所需的测量次数。这是通过使用封装在“测量矩阵”中的内部产品的一般概念来完成的。在这项工作中,我们专注于设计测量矩阵以及压缩感测恢复算法。我们考虑几种测量代码设计和恢复方案,并将其应用于特定系统。首先,我们考虑线性调频编码的压缩传感测量,该测量与联合设计的恢复算法一起用于计算有效的恢复。对于M次测量,O(M log M)恢复是可能的,与常规随机信号和恢复方法相比,速度显着提高。随后,我们在压缩感知的情况下考虑OFDM信道估计,并注意到测量矩阵仅限于子傅里叶矩阵的形式。通过确定性地选择一些导频音,我们提供了一种方法来表明用于恢复稀疏信道的合适矩阵。接下来,我们考虑如何使用压缩感知范式来构建新颖的无线系统。我们设计了一种用于异步通道上随机访问的多用户检测方案。对于此系统,我们开发了新的压缩感知恢复理论,并设计了适用于恢复活动用户稀疏集的密码本。最后,我们使用半双工硬件设计了虚拟的全双工自组织无线网络系统。在系统中,节点使用包含“侦听符号”的代码,在此期间设备会感测无线信道。当网络中的活动节点同时进行传输时,每个节点都将继承一个独特的压缩感知问题,以从其邻居中恢复数据。本论文的工作表明,在认真考虑应用的情况下,编码矩阵的压缩感知可以提供出色的性能改进和新颖的系统设计。

著录项

  • 作者

    Applebaum, Lorne.;

  • 作者单位

    Princeton University.;

  • 授予单位 Princeton University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 153 p.
  • 总页数 153
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号