JOURNAL ARTICLE

Super-exponential-estimator for fast blind channel identification of mobile radio fading channels

Abstract

An iterative algorithm for blind channel identification (no training symbols necessary) based on the super-exponential algorithm is shown. On the assumption of independent identically distributed (IID) data the algorithm has fast convergence properties. It is robust with respect to system overfit (supernumerarily assumed channel coefficients converge to zero) and influence of modest additive white Gaussian noise even in mixed-phase moving average channels. Despite of the use of fourth order cumulants the complexity of the algorithm is rather low compared with alternative blind methods. So the implementation on a signal processor (TMS320C40) is possible assuming GSM-like conditions.

Keywords:
Independent and identically distributed random variables Fading Estimator Algorithm Additive white Gaussian noise Computer science Channel (broadcasting) Overfitting Higher-order statistics Convergence (economics) Mathematics Speech recognition Statistics Signal processing Telecommunications Random variable Artificial intelligence Decoding methods Radar

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Citation History

Topics

Blind Source Separation Techniques
Physical Sciences →  Computer Science →  Signal Processing
Control Systems and Identification
Physical Sciences →  Engineering →  Control and Systems Engineering
Advanced Adaptive Filtering Techniques
Physical Sciences →  Engineering →  Computational Mechanics
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