  This paper considers the identification of FIR systems, where information
about the inputs and outputs of the system undergoes quantization into binary
values before transmission to the estimator. In the case where the thresholds
of the input and output quantizers can be adapted, but the quantizers have no
computation and storage capabilities, we propose identification schemes which
are strongly consistent for Gaussian distributed inputs and noises. This is
based on exploiting the correlations between the quantized input and output
observations to derive nonlinear equations that the true system parameters must
satisfy, and then estimating the parameters by solving these equations using
stochastic approximation techniques. If, in addition, the input and output
quantizers have computational and storage capabilities, strongly consistent
identification schemes are proposed which can handle arbitrary input and noise
distributions. In this case, some conditional expectation terms are computed at
the quantizers, which can then be estimated based on binary data transmitted by
the quantizers, subsequently allowing the parameters to be identified by
solving a set of linear equations. The algorithms and their properties are
illustrated in simulation examples.
