Estimation of MIMO Wireless Communications Channels: Approaches and Applications

MIMO 无线通信信道估计:方法和应用

基本信息

  • 批准号:
    0424145
  • 负责人:
  • 金额:
    $ 21万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2004
  • 资助国家:
    美国
  • 起止时间:
    2004-09-01 至 2008-08-31
  • 项目状态:
    已结题

项目摘要

Wireless channel is a challenging communications medium with relatively low capacity perunit bandwidth, random amplitude and phase uctuations due to multipath time-selective fading,intersymbol interference due to delay spread and multipaths, and interference from other usersdue to the broadcast nature of the radio channel. The physical link design goal is to achieve datarates close to the fundamental information capacity limits of the channel. Recent results haveshown that MIMO (multiple-input multiple-output) channels with multiple transmit and receiveantennas are capable of achieving enormous capacity gains over single antenna channels. Thishas spurred key advances in space-time processing to capitalize on increased Shannon capacity.Accurate knowledge of the CSI (channel state information) of MIMO systems is a prerequisite formost MIMO physical layer approaches. Traditionally a training sequence, in lieu of the informationsequence, is transmitted during the acquisition mode to enable the receiver to design an equalizer orestimate the channel in the presence of the aforementioned uncertainties. In the fast time-varyingcase, the training sequences may have to be transmitted periodically. For a given bandwidth, useof training sequences decreases the effective information rate. In blind channel estimation (systemidentification) and equalization no training sequences are available or used. In semi-blind channelestimation approaches, a combination of training and information sequence-based data is used sothat in addition to the training-based data, one also exploits the information in the rest of thereceived signal. In superimposed training-based approach the training sequence is \on" all the timeand is transmitted (at low power) concurrently with (superimposed on) the information sequence.This proposal is concerned with all such three techniques for channel estimation for both single userand multiple users systems and for both time-invariant frequency-selective channels and frequency-and time- selective fading channels.Identification of fast-varying nonstationary processes is best handled via structured nonsta-tionarities. Our initial focus is on time-varying channels described by a discrete-time complexexponential basis expansion model (CE-BEM) resulting in either a single-input multiple-output(SIMO) time-varying linear system for single user systems or a multiple-input multiple-output(MIMO) linear system for multiuser systems. For wireless channels such canonical models can bederived based on certain physical parameters such as signal bandwidth, channel Doppler spread andmultipath spread, up to some unknown time-invariant constants. Other modeling approaches suchas wavelet and polynomial bases, will also be considered. We are investigating blind, semi-blindand superimposed training-based system identification techniques for SIMO and MIMO channel es-timation, multiuser interference suppression, and equalization and detection of desired user's signalover asynchronous frequency- and/or time-selective fading channels.The intellectual merit of the proposed research lies in its focus on some fundamental modeling,signal design and channel estimation issues that cut across several applications areas (e.g. wirelesscommunications systems and networks, radio communications, and underwater acoustics). Boththeoretical and applications aspects are being considered.The broader impact of the project lies in graduate education of underrepresented groups,research at an EPSCoR institution, participation of students in professional society meetings, anddissemination of the research results through teaching at both undergraduate and graduate levels(particularly the courses that are part of the newly established Bachelor of Wireless Engineeringdegree program at Auburn University).A-1
无线信道是一种具有挑战性的通信介质,它具有单位带宽容量相对较低、多径时间选择性衰落导致的随机幅度和相位波动、延迟扩展和多径导致的符号间干扰以及无线信道的广播特性导致的来自其他用户的干扰。物理链路的设计目标是使数据速率接近信道的基本信息容量极限。最近的研究结果表明,MIMO(多输入多输出)信道与多个发送和接收天线是能够实现巨大的容量增益比单天线信道。MIMO系统的信道状态信息(CSI)是MIMO物理层处理的前提。传统上,在捕获模式期间发送训练序列代替信息序列,以使接收机能够设计均衡器,从而在存在上述不确定性的情况下对信道进行精确估计。在快速时变的情况下,训练序列可能必须周期性地发送。对于给定的带宽,训练序列的使用降低了有效信息速率。在盲信道估计(系统识别)和均衡中,没有训练序列可用或使用.在半盲信道估计方法中,使用基于训练和信息序列的数据的组合,使得除了基于训练的数据之外,还利用接收信号的其余部分中的信息。在基于叠加训练的方法中,训练序列始终处于”开“状态,(低功率时)同时该建议涉及用于单用户和多用户系统以及用于时不变频率选择性信道和频率和时间选择性衰落信道的信道估计的所有这三种技术。变化的非平稳过程最好通过结构化的非平稳性来处理。我们最初的重点是时变信道描述的离散时间复指数基扩展模型(CE-BEM)导致单用户系统的单输入多输出(SIMO)时变线性系统或多用户系统的多输入多输出(MIMO)线性系统。对于无线信道,这种正则模型可以基于某些物理参数,如信号带宽,信道多普勒扩展和多径扩展,直到一些未知的时不变常数。其他建模方法,如小波和多项式基地,也将被考虑。本文研究了异步频率和/或时间选择性衰落信道中SIMO和MIMO信道估计、多用户干扰抑制以及期望用户信号均衡和检测的盲、半盲和叠加训练系统辨识技术,其学术价值在于研究了一些基本的模型,信号设计和信道估计问题,跨越几个应用领域(例如,无线通信系统和网络,无线电通信和水下声学)。该项目的更广泛的影响在于代表性不足的群体的研究生教育,EPSCoR机构的研究,学生参与专业协会会议,以及通过本科和研究生教学传播研究成果(特别是奥本大学新设立的无线工程学士学位课程的一部分)。

项目成果

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Jitendra Tugnait其他文献

An Edge Exclusion Test for Complex Gaussian Graphical Model Selection
复杂高斯图形模型选择的边缘排除测试
Adaptive estimation and identification for discrete systems with Markov jump parameters
Sparse Graph Learning Under Laplacian-Related Constraints
  • DOI:
    10.1109/access.2021.3126675
  • 发表时间:
    2021-11
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Jitendra Tugnait
  • 通讯作者:
    Jitendra Tugnait
On Multisensor Detection of Improper Signals
Blind equalization and estimation of digital communication FIR channels using cumulant matching

Jitendra Tugnait的其他文献

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{{ truncateString('Jitendra Tugnait', 18)}}的其他基金

CIF:Small:Learning Sparse Vector and Matrix Graphs from Time-Dependent Data
CIF:小:从瞬态数据中学习稀疏向量和矩阵图
  • 批准号:
    2308473
  • 财政年份:
    2023
  • 资助金额:
    $ 21万
  • 项目类别:
    Standard Grant
EAGER: Learning Graphical Models of High-Dimensional Time Series
EAGER:学习高维时间序列的图形模型
  • 批准号:
    2040536
  • 财政年份:
    2020
  • 资助金额:
    $ 21万
  • 项目类别:
    Standard Grant
EAGER: Detection and Mitigation of Pilot Contamination Attacks and Related Issues in Massive MIMO Systems
EAGER:大规模 MIMO 系统中导频污染攻击及相关问题的检测和缓解
  • 批准号:
    1651133
  • 财政年份:
    2016
  • 资助金额:
    $ 21万
  • 项目类别:
    Standard Grant
CIF: Small: Complex-Valued Statistical Signal Processing with Dependent Data
CIF:小型:具有相关数据的复值统计信号处理
  • 批准号:
    1617610
  • 财政年份:
    2016
  • 资助金额:
    $ 21万
  • 项目类别:
    Standard Grant
Using the Channel State Information for Wireless Security Enhancement
使用信道状态信息增强无线安全性
  • 批准号:
    0823987
  • 财政年份:
    2008
  • 资助金额:
    $ 21万
  • 项目类别:
    Standard Grant
Frequency-Domain Approaches to Identification of Multiple-Input Multiple-Output Systems Given Time-Domain Data
给定时域数据的多输入多输出系统辨识的频域方法
  • 批准号:
    9912523
  • 财政年份:
    2000
  • 资助金额:
    $ 21万
  • 项目类别:
    Standard Grant
Spatio-Temporal Statistical Signal Processing For Blind Equalization and Source Separation
用于盲均衡和源分离的时空统计信号处理
  • 批准号:
    9803850
  • 财政年份:
    1998
  • 资助金额:
    $ 21万
  • 项目类别:
    Continuing Grant
Frequency-Domain Approaches To Control-Relevant System Identification
控制相关系统辨识的频域方法
  • 批准号:
    9504878
  • 财政年份:
    1995
  • 资助金额:
    $ 21万
  • 项目类别:
    Standard Grant
Higher Order Statistical Signal and Image Processing and Analysis
高阶统计信号和图像处理与分析
  • 批准号:
    9312559
  • 财政年份:
    1994
  • 资助金额:
    $ 21万
  • 项目类别:
    Continuing Grant
Blind Equalization and Channel Estimation in Data Communication Systems
数据通信系统中的盲均衡和信道估计
  • 批准号:
    9015587
  • 财政年份:
    1991
  • 资助金额:
    $ 21万
  • 项目类别:
    Continuing Grant

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