Abstract
A new adaptive multiple-shift correlation (MSC)-based blind channel equalizer (BCE) for multiple FIR channels is proposed. The performance of the MSC-based BCE under channel order mismatches due to small head and tail channel coefficient is investigated. The performance degradation is a function of the optimal output SINR, the optimal output power, and the control vector. This paper also proposes a simple but effective iterative method to improve the performance. Simulation examples are demonstrated to show the effectiveness of the proposed method and the analyses.
Highlights
Traditional adaptive equalizers are based on the periodic transmission of a known training data sequence in order to identify or equalize a distorted channel with intersymbol interference (ISI)
To reduce the degradation caused by the small channel coefficients, this paper proposes a simple iterative method
The order detection method is derived from the Multiple-shift correlation (MSC) matrix
Summary
Traditional adaptive equalizers are based on the periodic transmission of a known training data sequence in order to identify or equalize a distorted channel with intersymbol interference (ISI). The well-known approaches are the least-squares, the subspace, and the maximum likelihood [3, 8, 9] These blind equalizers were termed the two-step methods which estimate multiple channel parameters first and equalize the channels based on the estimated channel parameters. Multiple-shift correlation (MSC) of the signals can be used in a partially adaptive channel equalizer to achieve fast convergence speed and low computation load. These direct equalizers can be adaptive, leading to much simpler realization for practical implementation. We explore the relationship between the output signal-to-interference plus noise ratio (SINR) and the small head and tail terms of the FIR channels. We identify that the iterative method improves the equalization performance
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