Research AreasPublications

We address the challenge of optimizing the capacity-achieving input distribution for a multinomial channel under the constraint of limited input support size, which is a crucial aspect in the design of DNA storage systems. We propose an algorithm that further elaborates the Multidimensional Dynamic Assignment Blahut-Arimoto (M-DAB) algorithm. Our proposed algorithm integrates variational autoencoder for determining the optimal locations of input distribution, into the alternating optimization of the input distribution locations and weights.

Recent advancements in DNA storage show that composite DNA letters can significantly enhance storage capacity. We model this process as a multinomial channel and propose an optimization algorithm to determine its capacity-achieving input distribution (CAID) for an arbitrary number of output reads. Our empirical results match a scaling law that determines that the support size grows exponentially with capacity. In addition, we introduce a limited-support optimization algorithm that optimizes the input distribution under a restricted support size, making it more feasible for real-world DNA storage systems. We also extend our model to account for noise and study its effect on capacity and input design.

Code release for the paper: “Input Optimization in the Composite DNA Storage Channel” by Adir Kobovich and Nir Weinberger.