ATM communications network control by neural networks
Abstract
A learning method that uses neural networks for service quality control in the asynchronous transfer mode (ATM) communications network is described. Because the precise characteristics of the source traffic are not known and the service quality requirements change over time, building an efficient network controller which can control the network traffic is a difficult task. The proposed ATM network controller uses backpropagation neural networks for learning the relations between the offered traffic and service quality. The neural network is adaptive and easy to implement. A training data selection method called the leaky pattern table method is proposed to learn precise relations. The performance of the proposed controller is evaluated by simulation of basic call admission models.
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?
Hiramatsu demonstrated the feasibility of NN based approaches for accepting or rejecting requests, for resilience to dynamic changes in network traffic characteristics.
“He demonstrated the feasibility of NN based approaches for accepting or rejecting requests, for resilience to dynamic changes in network traffic characteristics.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [189]
Admission control has leveraged ML extensively in a variety of networks, including ATM networks, wireless networks, cellular networks, ad hoc networks, and next generation networks.
“As shown in Table 14, Admission control has leveraged ML extensively in a variety of networks, including ATM networks [95,189,190], wireless networks [8,36,359], cellular networks [66,67,281,372,458], ad hoc networks [452], and next generation networks [311].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [189]
Hiramatsu's early approach had unrealistic underlying assumptions: all calls had similar traffic characteristics, and cell loss rate was the sole QoS parameter.
“However, there were unrealistic underlying assumptions.First, all calls had similar traffic characteristics, that is, single bit or multi bitrate.Second, cell loss rate was the sole QoS parameter.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [189]
Hiramatsu was the first to propose NN based solutions controlling the admission of a service requesting resources for a basic call setup in ATM networks.
“To the best of our knowledge, Hiramatsu [189] was the first to propose NN based solutions controlling the admission of a service requesting resources for a basic call setup in ATM networks.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [189]
Cites
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686 · see citing works
A multilayered neural network controller
Psaltis, D., Sideris, A., & Yamamura, A. A. (1988) · IEEE Control Systems Magazine · doi:10.1109/37.1868 · Cited by 1,056 · see citing works
Feedback-error-learning neural network for trajectory control of a robotic manipulator
Miyamoto, H., Kawato, M., Setoyama, T., et al. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90030-5 · Cited by 447 · see citing works
Neural networks for routing communication traffic
Rauch, H. E., & Winarske, T. (1988) · IEEE Control Systems Magazine · doi:10.1109/37.1870 · Cited by 175 · see citing works
Zhang, L., & Thomopoulos, S. C. A. (1989) · doi:10.1109/ijcnn.1989.118375 · Cited by 66 · 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
Self-citation
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
Tong, H., & Brown, T. X. (2000) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.824799 · Cited by 85 · 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
Self-citationNeural-network connection-admission control for ATM networks
Cheng, R.-G., & Chang, C.-J. (1997) · IEE Proceedings - Communications · doi:10.1049/ip-com:19971088 · Cited by 20 · see citing works
Static vs. adaptive feedback congestion controller for ATM networks
Liu, Y.-C., & Douligeris, C. (2002) · Proceedings of GLOBECOM '95 · doi:10.1109/glocom.1995.500368 · Cited by 15 · see citing works
- Authors
- Hiramatsu, A.
- Affiliations
- NTT (Japan) (JP)
- Corresponding author
- Hiramatsu, A.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1990-03
- Volume
- 1
- Issue
- 1
- Pages
- 122-130
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2093815874
- Cited by
- 152
- References
- 6
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 1 · 2019: 1 · 2018: 2 · 2012: 2
- FWCI
- 5.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- asynchronous transfer mode, ATM networks, network traffic management, service quality control, call admission control, BP neural network, training data selection
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
