Cognitive Call Admission Control for VoIP over IEEE 802.11 Using Bayesian Networks
Abstract
In this paper we address the problem of provisioning Quality of Service (QoS) to Voice over IP applications in a Wireless LAN scenario based on the IEEE 802.11 standard. We propose the use of a Cognitive Network approach to design a Call Admission Control (CAC) scheme, according to which each user stores relevant information on its past network experience and then uses such information to build a Bayesian Network (BN), a probabilistic graphical model to describe the statistical relationships among network parameters. The BN is exploited to predict the voice call quality, as a function of the Link Layer conditions in the particular scenario considered. Such prediction on the present and future values of the QoS provided is directly exploited to design the cognitive CAC scheme, which is shown to significantly outperform state of the art CAC techniques in a realistic scenario.
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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 [372]
A BN can predict the probability distribution of certain network variables for better performance in admission control.
“As a remedy, a BN can predict the probability distribution of certain network variables for better performance in admission control [67,372].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [372]
Quer et al.'s BN model shows an accuracy of 95% in an ns-3 based simulation of a WLAN with both VoIP and TCP traffic.
“The authors evaluate the BN model via ns-3 based simulation of a WLAN, having both VoIP and TCP traffic, and show an accuracy of 95%.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [372]
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Admission Control of Video Sessions over Ad Hoc Networks Using Neural Classifiers
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- Authors
- Quer, G., Baldo, N., & Zorzi, M.
- Affiliations
- University of Padua (IT)
- Centre Tecnologic de Telecomunicacions de Catalunya (ES)
- Venue
- 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011
- Publisher
- IEEE
- Published
- 2011-12
- Pages
- 1-6
- ISSN
- 1930-529X (ISSN-L)
- OpenAlex
- W1999789320
- Cited by
- 10
- References
- 16
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 2 · 2018: 2 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Globecom (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- Army Research Office
- FP7 Information and Communication Technologies
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Communication Networks Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Keywords
- call admission control, cognitive networks, IP telephony, quality of service, Bayesian networks, IEEE 802.11, wireless LAN, probabilistic graphical models
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