27,817 research outputs found
Molecular communication in fluid media: The additive inverse Gaussian noise channel
We consider molecular communication, with information conveyed in the time of
release of molecules. The main contribution of this paper is the development of
a theoretical foundation for such a communication system. Specifically, we
develop the additive inverse Gaussian (IG) noise channel model: a channel in
which the information is corrupted by noise with an inverse Gaussian
distribution. We show that such a channel model is appropriate for molecular
communication in fluid media - when propagation between transmitter and
receiver is governed by Brownian motion and when there is positive drift from
transmitter to receiver. Taking advantage of the available literature on the IG
distribution, upper and lower bounds on channel capacity are developed, and a
maximum likelihood receiver is derived. Theory and simulation results are
presented which show that such a channel does not have a single quality measure
analogous to signal-to-noise ratio in the AWGN channel. It is also shown that
the use of multiple molecules leads to reduced error rate in a manner akin to
diversity order in wireless communications. Finally, we discuss some open
problems in molecular communications that arise from the IG system model.Comment: 28 pages, 8 figures. Submitted to IEEE Transactions on Information
Theory. Corrects minor typos in the first versio
Optimal Detection for Diffusion-Based Molecular Timing Channels
This work studies optimal detection for communication over diffusion-based
molecular timing (DBMT) channels. The transmitter simultaneously releases
multiple information particles, where the information is encoded in the time of
release. The receiver decodes the transmitted information based on the random
time of arrival of the information particles, which is modeled as an additive
noise channel. For a DBMT channel without flow, this noise follows the L\'evy
distribution. Under this channel model, the maximum-likelihood (ML) detector is
derived and shown to have high computational complexity. It is also shown that
under ML detection, releasing multiple particles improves performance, while
for any additive channel with -stable noise where (such as
the DBMT channel), under linear processing at the receiver, releasing multiple
particles degrades performance relative to releasing a single particle. Hence,
a new low-complexity detector, which is based on the first arrival (FA) among
all the transmitted particles, is proposed. It is shown that for a small number
of released particles, the performance of the FA detector is very close to that
of the ML detector. On the other hand, error exponent analysis shows that the
performance of the two detectors differ when the number of released particles
is large.Comment: 16 pages, 9 figures. Submitted for publicatio
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