Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
ICCS '02 Proceedings of the The 8th International Conference on Communication Systems - Volume 01
A stochastic gradient adaptive filter with gradient adaptive stepsize
IEEE Transactions on Signal Processing
Minimum mean squared error equalization using a priori information
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
A novel kurtosis driven variable step-size adaptive algorithm
IEEE Transactions on Signal Processing
MMSE and adaptive prediction of time-varying channels for OFDM systems
IEEE Transactions on Wireless Communications
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In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.