文件名称:TrackingofTimeVaryingChannelsUsingTwoStepLMSTypeA
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This paper presents a modified version of the twostep
least-mean-square (LMS)-type adaptive algorithm motivated
by the work of Gazor. We describe the nonstationary adaptation
characteristics of this modified two-step LMS (MG-LMS) algorithm
for the system identification problem. It ensures stable behavior
during convergence as well as improved tracking performance
in the smoothly time-varying environments.
least-mean-square (LMS)-type adaptive algorithm motivated
by the work of Gazor. We describe the nonstationary adaptation
characteristics of this modified two-step LMS (MG-LMS) algorithm
for the system identification problem. It ensures stable behavior
during convergence as well as improved tracking performance
in the smoothly time-varying environments.
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TrackingofTimeVaryingChannelsUsingTwoStepLMSTypeAdaptiveAlgorithm.pdf