Joint channel estimation and decoding with low-complexity iterative structures in time-varying fading channels

Yapıcı, Yavuz; Yılmaz, Ali Özgür

doi:10.1109/pimrc.2009.5450126

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Those parameters are always selected from extensive simulations, or from experience. It is clear that the choice of parameters would significantly influence the performance of these schemes [12].…”

Section: Variable Step Size Lms (Vss Lms) Algorithmmentioning

confidence: 99%

Adaptive Channel Estimation Algorithm For Multi Input Multi Output System – A Brief Review

Ingle¹,

Arun²

2016

IJOSCIENCE

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Channel estimation plays affective role on the performance of wireless and wire communication systems, since its knowledge is utilized to track and analysis the signal data symbols. Channel estimation is very important technique especially in wireless network systems where the wireless channel change over time, usually caused by transmitter and/or receiver being in motion and/or stable. In order to provide stability and high data receiving rates at the receiver, the system needs an accurate and minimum error estimate of the time-varying channel. Channel estimation is based on the training sequence of bits and which is unique for a certain transmitter and which is repeated in every transmitted burst. The modulated corrupted signal from the channel has to be undergoing the channel estimation using algorithms like Least Mean Squares, Normalized LMS, Variable Step Size LMS, Recursive Least Squares, Least Mean-Squares Newton Algorithm etc are used buffer and estimate the receiver end signal. In this paper a brief reviewing the different channel estimation algorithms for demodulation and comparing all of them with its output performances at various inputs signal and also proposed new algorithm for multi input multi output wireless communication system in noisy and motion environment. Index Terms— Channel estimation, Multiple Input Multiple Output, Least Mean Squares, Normalized LMS, Variable Step Size LMS, Recursive Least Squares, Least Mean-Squares Newton Algorithm, Mean square error.

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Section: Variable Step Size Lms (Vss Lms) Algorithmmentioning

confidence: 99%