The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Molecular Communication in Fluid Media: The Additive Inverse Gaussian Noise Channel
Open accessAbstract
In this paper, we consider molecular communication, with information conveyed in the time of release of molecules. These molecules propagate to the transmitter through a fluid medium, propelled by a positive drift velocity and Brownian motion. The main contribution of this paper is the development of a theoretical foundation for such a communication system; specifically, the additive inverse Gaussian noise (AIGN) channel model. In such a channel, the information is corrupted by noise that follows an IG distribution. We show that such a channel model is appropriate for molecular communication in fluid media. 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. Results are presented which suggest that this channel does not have a single quality measure analogous to signal-to-noise ratio in the additive white Gaussian noise 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, some open problems are discussed that arise from the IG channel model.
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
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
(2007) · Electronics: A First Course · doi:10.4324/9780080477527-64 · Cited by 24,923
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Brownian Motion and Stochastic Calculus
(2007) · Springer Finance · doi:10.1007/978-1-84628-696-4_13 · Cited by 7,422
ASH, R. B. (1966) · Cited by 3,990
Seidler, J. (1991) · Acta Applicandae Mathematicae · doi:10.1007/bf00046894 · Cited by 2,090
The Inverse Gaussian Distribution: Theory, Methodology, and Applications.
Ahsanullah, M., Chhikara, R. S., & Folks, J. L. (1990) · Journal of the American Statistical Association · doi:10.2307/2290048 · Cited by 582
A physical end-to-end model for molecular communication in nanonetworks
Pierobon, M., & Akyildiz, I. (2010) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2010.100509 · Cited by 533
Anantharam, V., & Verdu, S. (1996) · IEEE Transactions on Information Theory · doi:10.1109/18.481773 · Cited by 290
Microfluidics in Biotechnology
(2022) · Advances in Biochemical Engineering/Biotechnology · doi:10.1007/978-3-031-04188-4 · Cited by 266
Cited by
A Comprehensive Survey of Recent Advancements in Molecular Communication
Farsad, N., Yilmaz, H. B., Eckford, A., Chae, C.-B., & Guo, W. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2527741 · Cited by 968
Self-citationChannel Modeling for Diffusive Molecular Communication—A Tutorial Review
Jamali, V., Ahmadzadeh, A., Wicke, W., Noel, A., & Schober, R. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2919455 · Cited by 357
Metadata
- Authors
- Srinivas, K. V., Eckford, A. W., & Adve, R. S.
- Venue
- IEEE Transactions on Information Theory
- Published
- 2012-07
- Volume
- 58
- Issue
- 7
- Pages
- 4678-4692
- ISSN
- 0018-9448 (ISSN-L)
- 1557-9654
- OpenAlex
- W2077258873
- Cited by
- 391
- References
- 25
- FWCI
- 18.66
- 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: Molecular Communication in Fluid Media: The Additive Inverse Gaussian Noise Channel. https://app.cochat.ai/papers/10.1109/tit.2012.2193554
- The paper
- Srinivas, K. V., Eckford, A. W., & Adve, R. S. (2012). Molecular Communication in Fluid Media: The Additive Inverse Gaussian Noise Channel. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.2012.2193554
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.