Bounds on the Capacity of Random Insertion and Deletion-Additive Noise Channels
Mojtaba Rahmati, Tolga M. Duman

TL;DR
This paper derives new analytical lower bounds on the capacity of binary insertion and deletion channels, including models with channel noise, providing the first bounds for deletion-AWGN channels and improvements for insertion channels.
Contribution
It introduces the first analytical bounds for deletion-AWGN channels and improves existing bounds for deletion-substitution and small insertion probabilities.
Findings
First analytical bounds for deletion-AWGN channels.
Best available bounds for deletion-substitution channels.
Improved bounds for small insertion probabilities in Gallager's model.
Abstract
We develop several analytical lower bounds on the capacity of binary insertion and deletion channels by considering independent uniformly distributed (i.u.d.) inputs and computing lower bounds on the mutual information between the input and output sequences. For the deletion channel, we consider two different models: independent and identically distributed (i.i.d.) deletion-substitution channel and i.i.d. deletion channel with additive white Gaussian noise (AWGN). These two models are considered to incorporate effects of the channel noise along with the synchronization errors. For the insertion channel case we consider the Gallager's model in which the transmitted bits are replaced with two random bits and uniform over the four possibilities independently of any other insertion events. The general approach taken is similar in all cases, however the specific computations differ.…
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