  This letter presents a pioneering method that employs deep learning within a
probabilistic framework for the joint estimation of both direct and cascaded
channels in an ambient backscatter (AmBC) network comprising multiple tags. In
essence, we leverage an adversarial score-based generative model for training,
enabling the acquisition of channel distributions. Subsequently, our channel
estimation process involves sampling from the posterior distribution,
facilitated by the annealed Langevin sampling technique. Notably, our method
demonstrates substantial advancements over standard least square (LS)
estimation techniques, achieving performance akin to that of the minimum mean
square error (MMSE) estimator for the direct channel, and outperforming it for
the cascaded channels.
