MIMO Equalization With State-Space Channel Models

State-space models are proposed to represent multiple-input multiple-output (MIMO) frequency-selective wireless channels with the motivation of better model approximation and more robust channel equalization performance when the order of the channel model is lower that of the true channel. We develo...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2008-10, Vol.56 (10), p.5222-5231
Main Authors: Chengjin Zhang, Bitmead, R.R.
Format: Article
Language:English
Subjects:
Applied sciences
Channel equalization
Channel estimation
Channels
Convergence of numerical methods
Decision feedback equalizers
Design methodology
Detection, estimation, filtering, equalization, prediction
Economic models
Equalization
Equalizers
Error analysis
Errors
Exact sciences and technology
Finite impulse response filter
Frequency
Impulse response
Information, signal and communications theory
Mathematical analysis
Mathematical models
MIMO
Miscellaneous
multiple-input multiple-output (MIMO)
Numerical simulation
Receivers
Robustness
Signal and communications theory
Signal processing
Signal, noise
state-space models
Studies
Telecommunications and information theory
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Summary:State-space models are proposed to represent multiple-input multiple-output (MIMO) frequency-selective wireless channels with the motivation of better model approximation and more robust channel equalization performance when the order of the channel model is lower that of the true channel. We develop a simple framework under which the equalizers for state-space channel models can be designed using the existing methods for designing equalizers for finite impulse response (FIR) models. In particular, a MIMO minimum mean-squared error decision feedback equalizer is developed for state-space models. When only estimates of the channel are available to the receiver, the equalization performance is affected by the channel estimation accuracy. Because reduced-order state-space models can provide lower H 2 channel estimation error than reduced-length FIR models, state-space based equalizers typically exhibit significantly smaller symbol error rate than FIR-based ones. Thus, state-space channel models can be a more robust choice than FIR models in the presence of model order selection error. Numerical simulation also shows that adaptive state-space based receivers have slower but comparable convergence rates compared to FIR-based adaptive equalizers.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2008.929126