A least-squares approach to blind channel identification

TLDR

Conventional blind channel identification relies on known input statistics, but in practice the input model may be unknown or data insufficient. This work aims to blind‑identify multichannel FIR systems when the input is an unknown deterministic signal, eliminating the need for input statistical knowledge. We propose a purely output‑based blind identification algorithm and derive necessary and sufficient identifiability conditions for multichannel systems with deterministic inputs.

Abstract

Conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. In this paper, we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel FIR systems without requiring the knowledge of the input statistical model. A new blind identification algorithm based solely on the system outputs is proposed. Necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented.