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

无线通道是一种具有挑战性的通信介质，其容量相对较低，佩鲁尼特带宽，随机幅度和相位占相位，这是由于多学的时间选择性褪色，由于延迟扩散和多径径的间隔干扰以及从其他用户到无线电通道的广播性质而引起的。物理链路设计目标是实现接近渠道基本信息容量限制的数据。最近的结果是，具有多个发射和接收的MIMO（多输入多输出）通道能够在单个天线通道上实现巨大的容量增长。这是刺激了时空处理的关键进展，以利用增加的香农能力。对MIMO Systems的CSI（频道状态信息）的了解是一种先决条件的Formost Mimo物理层方法。传统上，训练顺序代替了信息序列，在采集模式下传输，以使接收器能够在存在上述不确定性的情况下设计均衡器。在快速的时间变化箱中，训练序列可能必须定期传输。对于给定的带宽，训练序列的使用降低了有效信息率。在盲道估计（系统识别）和均衡中，没有任何训练序列可用或使用。在半盲的频道估计方法中，除了基于训练的数据之外，还使用了基于训练和信息序列数据的组合，还可以利用这些信息在其他的信号中。在基于叠加训练的方法中，训练顺序是在“所有时间段（低功率下）同时（叠加）信息序列（叠加）。该提案与所有这三种技术有关单一用户和多个用户系统的渠道估计以及时间不变的频率频率和频率选择性的均可通过的频率及时的频率依赖的频率依赖的频道估算的所有这三种技术，均与快速的频道进行了识别。我们最初的重点是通过离散时间复杂的指数基础扩展模型（CE-BEM）所描述的，导致单个用户系统的单输入（SIMO）线性系统，用于单个用户系统或多个频道的多个频道，以供无线型号的频道使用。多普勒传播和穆尔图塔斯传播，直到一些未知的时间不变常数。还将考虑其他建模方法类似小波和多项式碱基。 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.无线通信系统和网络，无线电通信和水下声学）。 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