A Self-Learning Call Admission Control Scheme for CDMA Cellular Networks
Abstract
In the present paper, a call admission control scheme that can learn from the network environment and user behavior is developed for code division multiple access (CDMA) cellular networks that handle both voice and data services. The idea is built upon a novel learning control architecture with only a single module instead of two or three modules in adaptive critic designs (ACDs). The use of adaptive critic approach for call admission control in wireless cellular networks is new. The call admission controller can perform learning in real-time as well as in offline environments and the controller improves its performance as it gains more experience. Another important contribution in the present work is the choice of utility function for the present self-learning control approach which makes the present learning process much more efficient than existing learning control methods. The performance of our algorithm will be shown through computer simulation and compared with existing algorithms.
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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 [281]
Liu et al. demonstrate that their admission control mechanism outperforms non-adaptive admission control mechanisms with respect to GoS in CDMA cellular networks.
“Through simulation of cellular networks with two classes of services, the authors demonstrate that their admission control mechanism outperforms non-adaptive admission control mechanisms, with respect to GoS, in CDMA cellular networks.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [281]
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- Authors
- Liu, D., Zhang, Y., & Zhang, H.
- Affiliations
- University of Illinois Chicago (US)
- Northeastern University (CN)
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2005-09
- Volume
- 16
- Issue
- 5
- Pages
- 1219-1228
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2044922360
- Cited by
- 162
- References
- 36
- Citations by year
- 2024: 3 · 2023: 3 · 2022: 7 · 2021: 1 · 2019: 1 · 2018: 5 · 2017: 9 · 2016: 5 · 2015: 15 · 2014: 25 · 2013: 24 · 2012: 17
- FWCI
- 5.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Communication Networks Research
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- call admission control, CDMA networks, adaptive dynamic programming, self-learning control, utility functions, voice and data services, cellular networks, real-time learning
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Simulation, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Models, Statistical, Information Storage and Retrieval, Internet, Cell Phone
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