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Rewritable Channels With Data-Dependent Noise

We present some recent results on rewritable channels, that is, storage channels that admit optional reading and rewriting of the content at a given cost. This is a general class of channels that models many nonvolatile memories. We focus on the storage capacity of rewritable channels affected by data-dependent noise. We prove tight upper and lower bounds on the storage capacity of a simple yet significant channel model and suggest some simple capacity-achieving coding techniques. Lower bounds on the storage capacity of Gaussian rewritable channels with data-dependent noise are also shown.