5 research outputs found
Capacities and Optimal Input Distributions for Particle-Intensity Channels
This work introduces the particle-intensity channel (PIC) as a model for
molecular communication systems and characterizes 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 an 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.Comment: arXiv admin note: text overlap with arXiv:1705.0804