1,228 research outputs found
Diffusive Mobile Molecular Communications Over Time-Variant Channels
This letter introduces a formalism for modeling time-variant channels for
diffusive molecular communication systems. In particular, we consider a fluid
environment where one transmitter nano-machine and one receiver nano-machine
are subjected to Brownian motion in addition to the diffusive motion of the
information molecules used for communication. Due to the stochastic movements
of the transmitter and receiver nano-machines, the statistics of the channel
impulse response change over time. We show that the time-variant behaviour of
the channel can be accurately captured by appropriately modifying the diffusion
coefficient of the information molecules. Furthermore, we derive an analytical
expression for evaluation of the expected error probability of a simple
detector for the considered system. The accuracy of the proposed analytical
expression is verified via particle-based simulation of the Brownian motion.Comment: 4 pages, 3 figures, 1 table. Accepted for publication in IEEE
Communications Letters (Author's comment: Manuscript submitted Jan. 19, 2017;
revised Feb. 20, 2017; accepted Feb. 22, 2017
Diffusive MIMO Molecular Communications: Channel Estimation, Equalization and Detection
In diffusion-based communication, as for molecular systems, the achievable
data rate is low due to the stochastic nature of diffusion which exhibits a
severe inter-symbol-interference (ISI). Multiple-Input Multiple-Output (MIMO)
multiplexing improves the data rate at the expense of an inter-link
interference (ILI). This paper investigates training-based channel estimation
schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization
methods. Maximum likelihood and least-squares estimators of mean channel are
derived, and the training sequence is designed to minimize the mean square
error (MSE). Numerical validations in terms of MSE are compared with Cramer-Rao
bound derived herein. Equalization is based on decision feedback equalizer
(DFE) structure as this is effective in mitigating diffusive ISI/ILI.
Zero-forcing, minimum MSE and least-squares criteria have been paired to DFE,
and their performances are evaluated in terms of bit error probability. Since
D-MIMO systems are severely affected by the ILI because of short transmitters
inter-distance, D-MIMO time interleaving is exploited as countermeasure to
mitigate the ILI with remarkable performance improvements. The feasibility of a
block-type communication including training and data equalization is explored
for D-MIMO, and system-level performances are numerically derived.Comment: Accepted paper at IEEE transaction on Communicatio
Channel modeling for diffusive molecular communication - a tutorial review
Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC systems are expected to enable new revolutionary applications such as sensing of target substances in biotechnology, smart drug delivery in medicine, and monitoring of oil pipelines or chemical reactors in industrial settings. As for any other kind of communication, simple yet sufficiently accurate channel models are needed for the design, analysis, and efficient operation of MC systems. In this paper, we provide a tutorial review on mathematical channel modeling for diffusive MC systems. The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed. Deterministic models characterizing the expected number of molecules observed at the receiver and statistical models characterizing the actual number of observed molecules are developed. In addition, we provide channel models for timevarying MC systems with moving transmitters and receivers, which are relevant for advanced applications such as smart drug delivery with mobile nanomachines. For complex scenarios, where simple MC channel models cannot be obtained from first principles, we investigate simulation-driven and experiment-driven channel models. Finally, we provide a detailed discussion of potential challenges, open research problems, and future directions in channel modeling for diffusive MC systems
Channel Modeling for Diffusive Molecular Communication - A Tutorial Review
Molecular communication (MC) is a new communication engineering paradigm
where molecules are employed as information carriers. MC systems are expected
to enable new revolutionary applications such as sensing of target substances
in biotechnology, smart drug delivery in medicine, and monitoring of oil
pipelines or chemical reactors in industrial settings. As for any other kind of
communication, simple yet sufficiently accurate channel models are needed for
the design, analysis, and efficient operation of MC systems. In this paper, we
provide a tutorial review on mathematical channel modeling for diffusive MC
systems. The considered end-to-end MC channel models incorporate the effects of
the release mechanism, the MC environment, and the reception mechanism on the
observed information molecules. Thereby, the various existing models for the
different components of an MC system are presented under a common framework and
the underlying biological, chemical, and physical phenomena are discussed.
Deterministic models characterizing the expected number of molecules observed
at the receiver and statistical models characterizing the actual number of
observed molecules are developed. In addition, we provide channel models for
time-varying MC systems with moving transmitters and receivers, which are
relevant for advanced applications such as smart drug delivery with mobile
nanomachines. For complex scenarios, where simple MC channel models cannot be
obtained from first principles, we investigate simulation-driven and
experimentally-driven channel models. Finally, we provide a detailed discussion
of potential challenges, open research problems, and future directions in
channel modeling for diffusive MC systems.Comment: 40 pages; 23 figures, 2 tables; this paper is submitted to the
Proceedings of IEE
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