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We propose a novel system identification technique, based on a least-meansquare algorithm, allowing for the estimation of a linear channel by using anunknown-response measurement channel. The key of the technique is a memorylessnonlinear function working as uncoupling block between the estimated andobservation channels, conforming a Wiener-Hammerstein scheme. We prove thatthis estimation, only differing from the actual channel response by a scalingfactor and a temporal shift, does not depend on the observation channelbandwidth. As a consequence, this technique enables the usage of low-costmeasurement devices as feedback channel. We present numerical examples of themethod, supporting the proposal and displaying excellent results.

Linear Channel Estimation Based on a Low-Bandwidth Observation Channel with Unknown Response