TY - JOUR
T1 - Capacities and Optimal Input Distributions for Particle-Intensity Channels
AU - Farsad, Nariman
AU - Chuang, Will
AU - Goldsmith, Andrea
AU - Komninakis, Christos
AU - Medard, Muriel
AU - Rose, Christopher
AU - Vandenberghe, Lieven
AU - Wesel, Emily E.
AU - Wesel, Richard D.
N1 - Funding Information:
Manuscript received May 19, 2020; revised September 6, 2020; accepted October 5, 2020. Date of publication November 3, 2020; date of current version November 13, 2020. This work was supported in part by National Science Foundation (NSF) under Grant CCF-1911166; in part by the NSF Center for Science of Information under Grant CCF-0939370, in part by NSERC Discovery under Grant RGPIN-2020-04926; and in part by CFI John Evans Leaders Funds. This article was presented in part at ISIT 2017 [1] and the 2018 ITA Workshop [2]. The associate editor coordinating the review of this article and approving it for publication was N. Yang. (Corresponding author: Nariman Farsad.) Nariman Farsad is with the Department of Computer Science, Ryerson University, Toronto, ON M5B 2K3, Canada (e-mail: nfarsad@ryerson.edu).
Publisher Copyright:
© 2015 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Molecular communication
KW - channel capacity
KW - channel models
KW - optimal input
KW - optimization
KW - particle intensity channel
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U2 - 10.1109/TMBMC.2020.3035371
DO - 10.1109/TMBMC.2020.3035371
M3 - Article
AN - SCOPUS:85096126079
SN - 2332-7804
VL - 6
SP - 220
EP - 232
JO - IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
JF - IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
IS - 3
M1 - 9247283
ER -