Estimation of MIMO Wireless Communications Channels: Approaches and Applications

Auburn University, Auburn AL

Investigators

Abstract

Wireless channel is a challenging communications medium with relatively low capacity per unit bandwidth, random amplitude and phase uctuations due to multipath time-selective fading, intersymbol interference due to delay spread and multipaths, and interference from other users due to the broadcast nature of the radio channel. The physical link design goal is to achieve data rates close to the fundamental information capacity limits of the channel. Recent results have shown that MIMO (multiple-input multiple-output) channels with multiple transmit and receive antennas are capable of achieving enormous capacity gains over single antenna channels. This has 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 for most MIMO physical layer approaches. Traditionally a training sequence, in lieu of the information sequence, is transmitted during the acquisition mode to enable the receiver to design an equalizer or estimate the channel in the presence of the aforementioned uncertainties. In the fast time-varying case, the training sequences may have to be transmitted periodically. For a given bandwidth, use of training sequences decreases the effective information rate. In blind channel estimation (system identification) and equalization no training sequences are available or used. In semi-blind channel estimation approaches, a combination of training and information sequence-based data is used so that in addition to the training-based data, one also exploits the information in the rest of the received signal. In superimposed training-based approach the training sequence is \on" all the time and 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 user and 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 complex exponential 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 be derived based on certain physical parameters such as signal bandwidth, channel Doppler spread and multipath spread, up to some unknown time-invariant constants. Other modeling approaches such as wavelet and polynomial bases, will also be considered. We are investigating blind, semi-blind and superimposed training-based system identification techniques for SIMO and MIMO channel es- timation, multiuser interference suppression, and equalization and detection of desired user's signal over 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. wireless communications systems and networks, radio communications, and underwater acoustics). Both theoretical 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, and dissemination 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 Engineering degree program at Auburn University). A-1