Capacities and Optimal Input Distributions for Particle-Intensity Channels
Name
2005.10682.pdf
Description
Submitted version
Size
1.21 MB
Format
Adobe PDF
Checksum (MD5)
d1e227d8723544c4624eb579d0946088
Author(s)
Farsad, Nariman
Chuang, Will
Goldsmith, Andrea
Komninakis, Christos
Medard, Muriel
Rose, Christopher
Vandenberghe, Lieven
Wesel, Emily E
Wesel, Richard D
Date Issued
2020
Journal
IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
© 2015 IEEE. This work introduces the particle-intensity channel (PIC) as a new model for molecular communication systems that includes imperfections at both transmitter and receiver and provides a new characterization of the capacity limits as well as properties of the optimal (capacity-achieving) input distributions for such channels. In the PIC, the transmitter encodes information, in symbols of a given duration, based on the probability of particle release, and the receiver detects and decodes the message based on the number of particles detected during the symbol interval. In this channel, the transmitter may be unable to control precisely the probability of particle release, and the receiver may not detect all the particles that arrive. We model this channel using a generalization of the binomial channel and show that the capacity-achieving input distribution for this channel always has mass points at probabilities of particle release of zero and one. To find the capacity-achieving input distributions, we develop a novel and efficient algorithm we call dynamic assignment Blahut-Arimoto (DAB). For diffusive particle transport, we also derive the conditions under which the input with two mass points is capacity-achieving.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TMBMC.2020.3035371
