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Robustness of least-squares and subspace methods for blind channel identification/equalization algorithms with respect to channel undermodeling

Liavas Athanasios, Delmas, Fernand, Regalia, Phillip A., 1962-

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Summary

The least-squares and the subspace methods arewell known approaches for blind channel identification/equalization.When the order of the channel isknown, the algorithms are able to identify the channel,under the so-called length and zero conditions. Furthermore,in the noiseless case, the channel can be perfectlyequalized. Less is known about the performance ofthese algorithms in the cases in which the channel orderis underestimated. We partition the true impulse responseinto the significant part and the tails. We showthat the m-th order least-squares or subspace methodsestimate an impulse response which is “close” to them-th order significant part of the true impulse response.The closeness depends on the diversity of the m-th ordersignificant part and the size of the “unmodeled” part

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