A neural network controller for congestion control in ATM multiplexers
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The first ML-based approaches for congestion control were proposed in the context of ATM networks.
“the first ML-based approaches for congestion control were proposed in the context of asynchronous t ransfer mode (ATM) networks [175, 264, 284, 437].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [175]
Cites
(2012) · SpringerReference · doi:10.1007/springerreference_117344 · Cited by 1,047 · see citing works
Performance models of statistical multiplexing in packet video communications
Maglaris, V., Anastassiou, D., Sen, P., et al. (1988) · IEEE Transactions on Communications · doi:10.1109/26.2812 · Cited by 983 · see citing works
Analysis, modeling and generation of self-similar VBR video traffic
Garrett, M. W., & Willinger, W. (1994) · ACM SIGCOMM Computer Communication Review · doi:10.1145/190809.190339 · Cited by 953 · see citing works
Models for analysis of packet voice communications systems
Daigle, J. N., & Langford, J. D. (1986) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.1986.1146403 · Cited by 269 · see citing works
Grünenfelder, R., Cosmas, J., Manthorpe, S., et al. (1991) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.76626 · Cited by 147 · see citing works
Integration of ATM call admission control and link capacity control by distributed neural networks
Hiramatsu, A. (1991) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.103558 · Cited by 78 · see citing works
Integrated ATM traffic control by distributed neural networks
Takahashi, T., & Hiramatsu, A. (2005) · International Symposium on Switching · doi:10.1109/iss.1990.765714 · Cited by 21 · see citing works
A neural network approach towards adaptive congestion control in broadband ATM networks
Chen, X., & Leslie, I. (2002) · IEEE Global Telecommunications Conference (GLOBECOM) · doi:10.1109/glocom.1991.188367 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
A neural-fuzzy system for congestion control in ATM networks
Lee, S.-J., & Hou, C.-L. (2000) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.826942 · Cited by 45 · see citing works
- Authors
- Habib, I., Tarraf, A., & Saadawi, T.
- Affiliations
- City College of New York (US)
- ORCID
- Venue
- Computer Networks and ISDN Systems
- Publisher
- Elsevier BV
- Published
- 1997-02
- Volume
- 29
- Issue
- 3
- Pages
- 325-334
- ISSN
- 0169-7552 (ISSN-L)
- OpenAlex
- W1985009444
- Cited by
- 30
- References
- 8
- Citations by year
- 2022: 1 · 2019: 1 · 2018: 2 · 2017: 1 · 2014: 1 · 2012: 1
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Networks and ISDN Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- congestion control, ATM multiplexer, neural network controller, BP neural network, adaptive control, queueing, cell loss rate, performance index function, reinforcement learning
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
