Integration of ATM call admission control and link capacity control by distributed neural networks
Abstract
An adaptive call admission control using neural networks was recently proposed for asynchronous transfer mode (ATM) communications networks. The author proposes adaptive link capacity control using neural networks. Neural networks are trained to estimate the call loss rate from link capacity and observed traffic, and link capacity assignment is optimized by a random optimization method according to the estimated call loss rate. The integration of adaptive call admission control and adaptive link capacity control yields an efficient ATM traffic control system suitable for multimedia communication services with unknown traffic characteristics. Computer simulation results using a simple network model are also given to evaluate the accuracy and efficiency of the proposed method.>
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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 [190]
Hiramatsu later overcame these limitations by integrating admission control for calls and link capacity control in ATM networks using distributed NNs.
“Later, Hiramatsu [190] overcome these limitations, by integrating admission control for calls and link capacity control in ATM networks using distributed NNs.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [190]
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- Authors
- Hiramatsu, A.
- Affiliations
- NTT (Japan) (JP)
- Corresponding author
- Hiramatsu, A.
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 1991
- Volume
- 9
- Issue
- 7
- Pages
- 1131-1138
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W1997098001
- Cited by
- 78
- References
- 13
- Citations by year
- 2022: 1 · 2018: 1 · 2016: 2 · 2014: 1
- FWCI
- 2.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal on Selected Areas in Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Communication Networks Research
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- ATM networks, call admission control, neural network training, random optimization, traffic management, multimedia services
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