Finite-alphabet erasure channel ($Y=X\text{ with probability }1-f,Y=e\text{ with probability }f$)

A finite-alphabet erasure channel transmits the input symbol unchanged with probability $1-f$ and replaces it by a distinct erasure flag with probability $f$, independently of the symbol. Given the flag, the posterior remains the input distribution; otherwise the input is known. Thus its mutual information is $(1-f)H(X)$.

For a finite-alphabet erasure channel with $k$ input symbols, $I(X;Y)=(1-f)H(X)$. Maximizing Shannon entropy at the uniform input gives channel capacity $C=(1-f){\log }_{2}k$ bits per use, by the Shannon second coding theorem.