M. Rupp:

"Robust Design of Adaptive Equalizers";

IEEE Transactions on Signal Processing,60(2012), 4; 1612 - 1626.

Although equalizers promise to improve the signalto-

noise energy ratio, zero forcing equalizers are derived classically

in a deterministic setting minimizing intersymbol interference,

while minimum mean square error (MMSE) equalizer solutions

are derived in a stochastic context based on quadratic Wiener

cost functions. In this paper, we show that it is possible-and

in our opinion even simpler-to derive the classical results in a

purely deterministic setup, interpreting both equalizer types as

least squares solutions. This, in turn, allows the introduction of a

simple linear reference model for equalizers, which supports the

exact derivation of a family of iterative and recursive algorithms

with robust behavior. The framework applies equally to multiuser

transmissions and multiple-input multiple-output (MIMO) channels.

A major contribution is that due to the reference approach

the adaptive equalizer problem can equivalently be treated as

an adaptive system identification problem for which very precise

statements are possible with respect to convergence, robustness

and l2-stability. Robust adaptive equalizers are much more desirable

as they guarantee a much stronger form of stability than

conventional in the mean square sense convergence. Even some

blind channel estimation schemes can now be included in the form

of recursive algorithms and treated under this general framework.

Although equalizers promise to improve the signalto-

noise energy ratio, zero forcing equalizers are derived classically

in a deterministic setting minimizing intersymbol interference,

while minimum mean square error (MMSE) equalizer solutions

are derived in a stochastic context based on quadratic Wiener

cost functions. In this paper, we show that it is possible-and

in our opinion even simpler-to derive the classical results in a

purely deterministic setup, interpreting both equalizer types as

least squares solutions. This, in turn, allows the introduction of a

simple linear reference model for equalizers, which supports the

exact derivation of a family of iterative and recursive algorithms

with robust behavior. The framework applies equally to multiuser

transmissions and multiple-input multiple-output (MIMO) channels.

A major contribution is that due to the reference approach

the adaptive equalizer problem can equivalently be treated as

an adaptive system identification problem for which very precise

statements are possible with respect to convergence, robustness

and l2-stability. Robust adaptive equalizers are much more desirable

as they guarantee a much stronger form of stability than

conventional in the mean square sense convergence. Even some

blind channel estimation schemes can now be included in the form

of recursive algorithms and treated under this general framework.

Blind channel estimation, convergence, iterative adaptive filters, least mean squares, linear equalizers, l2-stability, minimum mean square error (MMSE), recursive adaptive filters, reference modeling, robustness, zero forcing (ZF)

http://dx.doi.org/10.1109/TSP.2011.2180717

http://publik.tuwien.ac.at/files/PubDat_207068.pdf

Project Head Markus Rupp:

Signal and Information Processing in Science and Engineering II: Theory and Implementation of Distributed Algorithms

Created from the Publication Database of the Vienna University of Technology.