TY - GEN
T1 - Bee Identification Problem for DNA Strands
AU - Chrisnata, Johan
AU - Kiah, Han Mao
AU - Vardy, Alexander
AU - Yaakobi, Eitan
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Motivated by DNA-based applications, we generalize the bee identification problem proposed by Tandon et al. (2019). In this setup, we transmit all M codewords from a codebook over some channel and each codeword results in N noisy outputs. Then our task is to identify each codeword from the MN noisy outputs.First, via a reduction to a minimum-cost flow problem on a related flow network GN, we show that the problem can be solved in O(M3) time in the worst case. Next, we consider the deletion channel and study the expected number of edges in the network GN. Specifically, we obtain closed expressions for this quantity for certain codebooks and when the codebook comprises all binary words, we show that this quantity is sub-quadratic when the deletion probability is less than 1/2. This then implies that the expected running time for this codebook is o(M3). For other codebooks, we develop methods to compute the expected number of edges efficiently. Finally, we adapt classical peeling-decoding techniques to reduce the number of nodes and edges in GN.
AB - Motivated by DNA-based applications, we generalize the bee identification problem proposed by Tandon et al. (2019). In this setup, we transmit all M codewords from a codebook over some channel and each codeword results in N noisy outputs. Then our task is to identify each codeword from the MN noisy outputs.First, via a reduction to a minimum-cost flow problem on a related flow network GN, we show that the problem can be solved in O(M3) time in the worst case. Next, we consider the deletion channel and study the expected number of edges in the network GN. Specifically, we obtain closed expressions for this quantity for certain codebooks and when the codebook comprises all binary words, we show that this quantity is sub-quadratic when the deletion probability is less than 1/2. This then implies that the expected running time for this codebook is o(M3). For other codebooks, we develop methods to compute the expected number of edges efficiently. Finally, we adapt classical peeling-decoding techniques to reduce the number of nodes and edges in GN.
UR - http://www.scopus.com/inward/record.url?scp=85136245086&partnerID=8YFLogxK
U2 - 10.1109/ISIT50566.2022.9834414
DO - 10.1109/ISIT50566.2022.9834414
M3 - منشور من مؤتمر
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 969
EP - 974
BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022
T2 - 2022 IEEE International Symposium on Information Theory, ISIT 2022
Y2 - 26 June 2022 through 1 July 2022
ER -