Estimation of MIMO Wireless Communications Channels: Approaches and Applications

MIMO 无线通信信道估计：方法和应用

• 批准号: 0424145
• 负责人: Jitendra Tugnait
• 金额: $ 21万
• 依托单位: Auburn University
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0424145&HistoricalAwards=false
• 关键词: Estimation; MIMO; Wireless; Communications; Channels

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，其单位带宽容量相对较低，由于多径时间选择性衰落而产生随机幅度和相位波动，由于延迟扩展和多径而产生符号间干扰，以及由于无线电信道的广播性质而来自其他用户的干扰。物理链路设计目标是实现接近信道基本信息容量限制的数据速率。最近的结果表明，具有多个发射和接收天线的 MIMO（多输入多输出）信道能够比单天线信道实现巨大的容量增益。这刺激了时空处理方面的重大进步，以利用增加的香农容量。准确了解 MIMO 系统的 CSI（信道状态信息）是大多数 MIMO 物理层方法的先决条件。传统上，在捕获模式期间传输训练序列来代替信息序列，以使接收器能够设计均衡器或在存在上述不确定性的情况下估计信道。在快速时变的情况下，可能必须周期性地发送训练序列。对于给定的带宽，训练序列的使用会降低有效信息率。在盲信道估计（系统识别）和均衡中，没有可用或使用的训练序列。在半盲信道估计方法中，使用训练和基于信息序列的数据的组合，使得除了基于训练的数据之外，还利用其余接收信号中的信息。在基于叠加训练的方法中，训练序列始终处于开启状态，并与信息序列（叠加在其上）同时传输（以低功率）。该提案涉及单用户和多用户信道估计的所有这三种技术。用户系统以及时不变的频率选择性信道和频率和时间选择性衰落信道。快速变化的非平稳过程的识别最好通过结构化非平稳性来处理。我们最初的重点是时变。由离散时间复数指数基扩展模型 (CE-BEM) 描述的信道，导致单用户系统的单输入多输出 (SIMO) 时变线性系统或多输入多输出 (MIMO) 线性系统对于无线信道，可以基于某些物理参数（例如信号带宽、信道多普勒扩展和多径扩展，直至一些未知的时不变常数）来导出此类规范模型。还将考虑其他建模方法，例如小波和多项式基。我们正在研究基于盲、半盲和叠加训练的系统识别技术，用于 SIMO 和 MIMO 信道估计、多用户干扰抑制以及异步频率和/或时间选择性衰落信道上所需用户信号的均衡和检测。拟议研究的优点在于其重点关注跨越多个应用领域（例如无线通信系统和网络、无线电通信和水下声学）的一些基本建模、信号设计和信道估计问题。理论和应用方面都在考虑之中。该项目更广泛的影响在于代表性不足群体的研究生教育、EPSCoR 机构的研究、学生参加专业协会会议以及通过本科生和研究生水平的教学传播研究成果（特别是奥本大学新设立的无线工程学学士学位课程的一部分）。A-1