Locally Balanced Constraints

Ryan Gabrys, Han Mao Kiah, Alexander Vardy, Eitan Yaakobi, Yiwei Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Three new constraints are introduced in this paper. These constraints are characterized by limitations on the Hamming weight of every subword of some fixed even length l. In the (l, δ)-locally-balanced constraint, the Hamming weight of every length-l subword is bounded between l/2 - δ and l/2 + δ. The strong-(l,δ)-locally-balanced constraint imposes the locally- balanced constraint for any subword whose length is at least l. Lastly, the Hamming weight of every length-l subword which satisfies the (l, δ)-locally-bounded constraint is at most l/2 - δ. It is shown that the capacity of the strong-(l, δ)-locally-balanced constraint does not depend on the value of l and is identical to the capacity of the (2δ + 1)-RDS constraint. The latter constraint limits the difference between the number of zeros and ones in every prefix of the word to be at most 2δ + 1. This value is also a lower bound on the capacity of the (l, δ)-locally-balanced constraint, while a corresponding upper bound is given as well. Lastly, it is shown that if δ is not large enough, namely for δ < l/2, then the capacity of the (l, δ)-locally-bounded constraint approaches 1 as l increases.

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
Number of pages6
ISBN (Electronic)9781728164328
StatePublished - Jun 2020
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: 21 Jul 202026 Jul 2020

Publication series

NameIEEE International Symposium on Information Theory - Proceedings


Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
Country/TerritoryUnited States
CityLos Angeles

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics


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