TY - GEN
T1 - Ldpc codes achieve list decoding capacity
AU - Mosheiff, Jonathan
AU - Resch, Nicolas
AU - Ron-Zewi, Noga
AU - Silas, Shashwat
AU - Wootters, Mary
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - We show that Gallager's ensemble of Low-Density Parity Check (LDPC) codes achieves list-decoding capacity with high probability. These are the first graph-based codes shown to have this property. This result opens up a potential avenue towards truly linear-time list-decodable codes that achieve list-decoding capacity. Our result on list decoding follows from a much more general result: Any local property satisfied with high probability by a random linear code is also satisfied with high probability by a random LDPC code from Gallager's distribution. Local properties are properties characterized by the exclusion of small sets of codewords, and include list-decoding, list-recovery and average-radius list-decoding. In order to prove our results on LDPC codes, we establish sharp thresholds for when local properties are satisfied by a random linear code. More precisely, we show that for any local property mathcal{P}, there is some R{ast} so that random linear codes of rate slightly less than R{ast} satisfy mathcal{P} with high probability, while random linear codes of rate slightly more than R{ast} with high probability do not. We also give a characterization of the threshold rate R{ast}. This is an extended abstract. The full version is available at https://arxiv.org/abs/1909.06430
AB - We show that Gallager's ensemble of Low-Density Parity Check (LDPC) codes achieves list-decoding capacity with high probability. These are the first graph-based codes shown to have this property. This result opens up a potential avenue towards truly linear-time list-decodable codes that achieve list-decoding capacity. Our result on list decoding follows from a much more general result: Any local property satisfied with high probability by a random linear code is also satisfied with high probability by a random LDPC code from Gallager's distribution. Local properties are properties characterized by the exclusion of small sets of codewords, and include list-decoding, list-recovery and average-radius list-decoding. In order to prove our results on LDPC codes, we establish sharp thresholds for when local properties are satisfied by a random linear code. More precisely, we show that for any local property mathcal{P}, there is some R{ast} so that random linear codes of rate slightly less than R{ast} satisfy mathcal{P} with high probability, while random linear codes of rate slightly more than R{ast} with high probability do not. We also give a characterization of the threshold rate R{ast}. This is an extended abstract. The full version is available at https://arxiv.org/abs/1909.06430
KW - LDPC, List Decoding, Local Property, Thershold
UR - http://www.scopus.com/inward/record.url?scp=85097921523&partnerID=8YFLogxK
U2 - 10.1109/FOCS46700.2020.00050
DO - 10.1109/FOCS46700.2020.00050
M3 - Conference contribution
AN - SCOPUS:85097921523
T3 - Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
SP - 458
EP - 469
BT - Proceedings - 2020 IEEE 61st Annual Symposium on Foundations of Computer Science, FOCS 2020
PB - Institute of Electrical and Electronics Engineers
T2 - 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020
Y2 - 16 November 2020 through 19 November 2020
ER -