The Insertion List-Decoding Capacity and an Improved Bound on the Deletion List-Decoding Capacity

Informally, the capacity of list-decoding in a given adversarial error model is the largest rate at which we can list-decode with list size polynomial in the block length. The capacity of list-decoding from insertions and deletions is a basic, yet poorly understood, aspect of coding against synchronization errors. For example, when dealing with a $\delta>1/2$ fraction of insertions, the best known lower bounds give little more than the fact that the capacity is positive. Beyond that regime we also only have loose bounds, with the lower bounds stemming from the analysis of uniformly random codes. We make progress in our understanding of the limits of list-decoding binary codes from insertions and deletions. We show that the capacity of list-decoding from a $\delta$-fraction of insertions is exactly \begin{equation*} (1+\delta)\left(1-h\left(\frac{\delta}{1+\delta}\right)\right) \end{equation*} for all $\delta\in[0,1]$, achieved with high probability by a code sampled according to a symmetric $2$-state Markov chain. Curiously, we complement this by showing that such an approach does not beat uniformly random coding in list-decoding from deletions. We also give an improved upper bound on the capacity of list-decoding from a $\delta$-fraction of deletions, showing in particular that it behaves like $1-h(\delta)$ when $\delta\to 0$. This matches the asymptotic behavior of the capacity of the binary deletion channel for vanishing deletion probability.