Deterministic Identification for Molecular Communications Over the Poisson Channel

Mohammad Javad Salariseddigh, Vahid Jamali, Uzi Pereg, Holger Boche, Christian Deppe, Robert Schober

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Various applications of molecular communications (MC) are event-triggered, and, as a consequence, the prevalent Shannon capacity may not be the right measure for performance assessment. Thus, in this paper, we motivate and establish the identification capacity as an alternative metric. In particular, we study deterministic identification (DI) for the discrete-time Poisson channel (DTPC), subject to an average and a peak molecule release rate constraint, which serves as a model for MC systems employing molecule counting receivers. It is established that the number of different messages that can be reliably identified for this channel scales as 2 (n n)R , where n and R are the codeword length and coding rate, respectively. Lower and upper bounds on the DI capacity of the DTPC are developed. The obtained large capacity of the DI channel sheds light on the performance of natural DI systems such as natural olfaction, which are known for their extremely large chemical discriminatory power in biology. Furthermore, numerical results for the empirical miss-identification and false identification error rates are provided for finite length codes. This allows us to characterize the behaviour of the error rate for increasing codeword lengths, which complements our theoretically-derived scale for asymptotically large codeword lengths.

Original languageEnglish
Pages (from-to)408-424
Number of pages17
JournalIEEE Transactions on Molecular, Biological, and Multi-Scale Communications
Volume9
Issue number4
DOIs
StatePublished - 1 Dec 2023

Keywords

  • Channel capacity
  • Poisson channel
  • deterministic identification
  • molecular communication

Fingerprint

Dive into the research topics of 'Deterministic Identification for Molecular Communications Over the Poisson Channel'. Together they form a unique fingerprint.

Cite this