Convergence of stochastic-approximation-based algorithms for blind channel identification

Authors:
Han-Fu Chen;Xi-Ren Cao;Jie Zhu
Affiliations:
Inst. of Syst. Sci., Acad. Sinica, Beijing;-;-
Venue:
IEEE Transactions on Information Theory
Year:
2006

Citing 0
Cited 3

Blind Channel Identification Based on Noisy Observation by Stochastic Approximation Method

Journal of Global Optimization
Time delay estimation in room acoustic environments: an overview

EURASIP Journal on Applied Signal Processing
Fast communication: Adaptive multichannel blind identification using manifold optimization

Signal Processing

Quantified Score

Hi-index	754.84

Visualization

Abstract

We develop adaptive algorithms for multichannel (single-input-multiple-output, or SIMO) blind identification with both statistic and deterministic models. In these algorithms, the estimates are continuously improved while receiving new signals. Therefore, the algorithms can track the channel continuously and thus are amenable to real applications such as wireless communications. At each step, only a small amount of computation is involved. The algorithms are based on stochastic-approximation methods. The convergence properties of these algorithms are proved. Simulation examples are presented to show the performance of the algorithms