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Channel Input Distribution Estimation Using a Minimum I-divergence Algorithm

Given a channel with a known transition probability, we consider the problem of finding the input distribution that most closely achieves a desired output distribution. We pose the problem as a linear inverse problem subject to nonnegativity constraints, and employ an iterative algorithm for minimizing Csiszar’s I-divergence between the desired channel output and the channel output derived from an estimated channel input. We also show how to modify the algorithm to incorporate symmetry constraints on the input distribution. Particular examples involving Rician channels are shown.