RLS channel estimation with superimposed training sequence in OFDM systems

Junping Li Junping Li,Shouyin Liu Shouyin Liu,Jie Ma Jie Ma

doi:10.1109/icct.2008.4716183

Junping Li Junping Li, Shouyin Liu Shouyin Liu + Show 1 more

https://doi.org/10.1109/icct.2008.4716183

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Publication Date: Nov 1, 2008

Citations: 13

Affiliation: Central China Normal University

Abstract

In this paper, A Recursive Least Squares (RLS) channel estimator with improved decision-directed algorithm (referred as DDA2-RLS) is proposed based on the superimposed training sequence in orthogonal frequency division multiplexing (OFDM) systems. The DDA2-RLS is exploited to further eliminate the interference driven by the superimposed unknown information data. Then, the theoretical analysis for DDA2-RLS algorithm with superimposed training sequence that uses the constant Pseudo-Noise (PN) sequence is given. It is shown that the proposed DDA2-RLS algorithm can improve the channel estimation performance compared with the original RLS and decision-directed algorithm (DDA) RLS algorithms. Simulations results demonstrate the effectiveness of the proposed DDA2-RLS, and the performance is close to the theoretical analysis compared with original RLS and DDA-RLS algorithms.

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