Abstract
This paper deals with the problem of blind equalizations based on effective channel order determination for multiple FIR channels. Most popular order determination methods use the eigenvalue decomposition (EVD) technique with an overmodeled data correlation matrix. However, performing the EVD consumes huge computation resources. In this paper, we consider the channel with infinite small leading and tailing terms which is natural for measured microwave radio channels, and develop a computationally simple method for effective channel order determination. Based on multiple-shift property of a data correlation matrix, a new performance index is analyzed. The channel order is determined if the performance index is greater than a threshold. To select the threshold, we model the performance index as an -distributed random variable. For a specified confidence level, the threshold can be found from the table. This proposed method does not require EVD, the computation load is much lower than that of the EVD-based methods.
Highlights
Blind adaptive equalization of multiple FIR channels without training data available was studied intensively in the literature
Due to the nature of practical microwave radio channels having long small leading and tailing channel terms, it has been shown that blind channel equalization algorithms should attempt to model only the significant part of the channel composed of the large impulse response terms [13]
On the consideration of the worst case of the small channel parameters, we find that trace of the multiple-shift correlation matrix and its complex conjugation can be approximately expressed in the worst case as a comparison of the first and last terms of the significant part of the channel with the small leading and tailing terms
Summary
Blind adaptive equalization of multiple FIR channels without training data available was studied intensively in the literature. Researches show the AIC and MDL in the measured microwave radio channels are very sensitive to variations in the signal to noise ratio (SNR) and the number of data samples [12] This prohibits their application for channel order estimation. Existence of a gap between two consecutive eigenvalues makes the channel order being determined Simulations show that this method provides robustness to variations in the SNR and the number of samples. The proposed method is developed based on using multiple-shift property of the data correlation matrix. We analyze a channel model with infinite small leading and tailing terms and compute two multiple-shift correlation matrices with the shift delay index equal to and greater than the channel order. The required computation load mainly comes from trace operation which is much lower than that of the EVDbased methods
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