The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Channel Modeling for Diffusive Molecular Communication—A Tutorial Review
Open accessAbstract
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 the 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 the simulation- 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Wireless Communications: Principles and Practice
Rappaport, T. S. (1996) · Cited by 17,370
Diffusion, mass transfer in fluid systems
(1997) · Choice Reviews Online · doi:10.5860/choice.35-0927 · Cited by 4,930
Pearson Correlation Coefficient
Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009) · Springer Topics in Signal Processing · doi:10.1007/978-3-642-00296-0_5 · Cited by 3,059
Survey on Free Space Optical Communication: A Communication Theory Perspective
Khalighi, M. A., & Uysal, M. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2329501 · Cited by 2,466
Berg, H. C. (2025) · Princeton University Press eBooks · doi:10.1515/9780691279886 · Cited by 2,367
Approximate accelerated stochastic simulation of chemically reacting systems
Gillespie, D. T. (2001) · The Journal of Chemical Physics · doi:10.1063/1.1378322 · Cited by 1,884
Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels
Gibson, M. A., & Bruck, J. (2000) · The Journal of Physical Chemistry A · doi:10.1021/jp993732q · Cited by 1,643
Diffusion: Mass Transfer in Fluid Systems
Cussler, E. L. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511805134 · Cited by 1,356
Nanonetworks: A new communication paradigm
Akyildiz, I. F., Brunetti, F., & Blázquez, C. (2008) · Computer Networks · doi:10.1016/j.comnet.2008.04.001 · Cited by 1,260
Cited by
Two-Dimensional Field-Effect Transistor Sensors: The Road toward Commercialization
Dai, C., Liu, Y., & Wei, D. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00924 · Cited by 303
Metadata
- Authors
- Jamali, V., Ahmadzadeh, A., Wicke, W., Noel, A., & Schober, R.
- Venue
- Proceedings of the IEEE
- Published
- 2019-07
- Volume
- 107
- Issue
- 7
- Pages
- 1256-1301
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2905312169
- Cited by
- 357
- References
- 92
- FWCI
- 13.10
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Channel Modeling for Diffusive Molecular Communication—A Tutorial Review. https://app.cochat.ai/papers/10.1109/jproc.2019.2919455
- The paper
- Jamali, V., Ahmadzadeh, A., Wicke, W., Noel, A., & Schober, R. (2019). Channel Modeling for Diffusive Molecular Communication—A Tutorial Review. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2019.2919455
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.