QoS Provisioning Dynamic Connection-Admission Control for Multimedia Wireless Networks Using a Hopfield Neural Network
Abstract
This paper presents a quality-of-service (QoS) provisioning dynamic connection-admission control (CAC) algorithm for multimedia wireless networks. A multimedia connection consists of several substreams (i.e., service classes), each of which presets a range of feasible QoS levels (e.g., data rates). The proposed algorithm is mainly devoted to finding the best possible QoS levels for all the connections (i.e., QoS vector) that maximize resource utilization by fairly distributing wireless resources among the connections while maximizing the statistical multiplexing gain (i.e., minimizing the blocking and dropping probabilities). In the case of congestion (overload), the algorithm uniformly degrades the QoS levels of the existing connections (but only slightly) in order to spare some resources for serving new or handoff connections, thereby naturally minimizing the blocking and dropping probabilities (it amounts to maximizing the statistical multiplexing gain). The algorithm employs a Hopfield neural network (HNN) for finding a QoS vector. The problem itself is formulated as a multi-objective optimization problem. Hardware-based HNN exhibits high (computational) speed that permits real time running of the CAC algorithm. Simulation results show that the algorithm can maximize resource utilization and maintain fairness in resource sharing, while maximizing the statistical multiplexing gain in providing acceptable service grades. Furthermore, the results are relatively insensitive to handoff rates.
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?
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 [8]
Ahn et al.'s HNN does not require any training and can easily adapt to dynamic network conditions.
“Most importantly, the HNN does not require any training, and can easily adapt to dynamic network conditions.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [8]
Ahn et al.'s admission control mechanism achieves significant gains in ATM networks in terms of minimizing blocking and dropping probabilities and maximizing fairness in resource allocation.
“This admission control mechanism achieves significant gains in ATM networks, in terms of minimizing the blocking and dropping probabilities and maximizing fairness in resource allocation.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [8]
Cites
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918 · see citing works
“Neural” computation of decisions in optimization problems
Hopfield, J. J., & Tank, D. W. (1985) · Biological Cybernetics · doi:10.1007/bf00339943 · Cited by 6,035 · see citing works
Rate control for communication networks: shadow prices, proportional fairness and stability
Kelly, F. P., Maulloo, A. K., & Tan, D. K. H. (1998) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2600523 · Cited by 5,098 · see citing works
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
Jang, J. R., & Sun, C. (1996) · Cumulative Index of Computer Aided Architectural Design · Cited by 4,629 · see citing works
Rate control for communication networks: shadow prices, proportional fairness and stability
Kelly, F. P., Maulloo, A. K., & Tan, D. K. H. (1998) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600523 · Cited by 2,403 · see citing works
Jang, J. R., Sun, C., & Mizutani, E. (1997) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1997.633847 · Cited by 1,681 · see citing works
Elementary Numerical Analysis.
G., W., & ATKINSON, K. E. (1994) · Mathematics of Computation · doi:10.2307/2153423 · Cited by 1,255 · see citing works
On optimal call admission control in cellular networks
Ramjee, R., Towsley, D., & Nagarajan, R. (1997) · Wireless Networks · doi:10.1023/a:1019172226345 · Cited by 431 · see citing works
Neural networks for shortest path computation and routing in computer networks
Ali, M., & Kamoun, F. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.286889 · Cited by 272 · see citing works
Predictive and adaptive bandwidth reservation for hand-offs in QoS-sensitive cellular networks
Choi, S., & Shin, K. G. (1998) · ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (SIGCOMM) · doi:10.1145/285237.285278 · Cited by 228 · see citing works
Neural network parallel computing
Takefujo, Y. (1992) · Kluwer international series in engineering and computer science · Cited by 159 · see citing works
Neural Network Parallel Computing
Takefuji, Y. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3642-0 · Cited by 148 · see citing works
Adaptive bandwidth reservation and admission control in QoS-sensitive cellular networks
Choi, S., & Shin, K. G. (2002) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2002.1036063 · Cited by 139 · see citing works
Predictive QoS-based admission control for multiclass traffic in cellular wireless networks
Epstein, B., & Schwartz, M. (2000) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.840209 · Cited by 139 · see citing works
Reservation strategies for multi-media traffic in a wireless environment
Epstein, B., & Schwartz, M. (2002) · doi:10.1109/vetec.1995.504850 · Cited by 133 · see citing works
Network management and control issues in multimedia wireless networks
Schwartz, M. (1995) · IEEE Personal Communications · doi:10.1109/98.388925 · Cited by 122 · see citing works
Hopfield neural networks for affine invariant matching
Li, W. J., & Lee, T. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.963776 · Cited by 87 · see citing works
Predictive and adaptive bandwidth reservation for hand-offs in QoS-sensitive cellular networks
Choi, S., & Shin, K. G. (1998) · ACM SIGCOMM Computer Communication Review · doi:10.1145/285243.285278 · Cited by 87 · see citing works
A QoS-provisioning neural fuzzy connection admission controller for multimedia high-speed networks
Cheng, R., Chang, C., & Lin, L.-F. (1999) · IEEE/ACM Transactions on Networking · doi:10.1109/90.759332 · Cited by 83 · see citing works
Shortest path routing algorithmusing Hopfield neural network
Ahn, C. W., Ramakrishna, R. S., Kang, C. G., et al. (2001) · Electronics Letters · doi:10.1049/el:20010800 · Cited by 67 · see citing works
Self-citationImproving call admission policies in wireless networks
Ho, C., & Lea, C. (1999) · Wireless Networks · doi:10.1023/a:1019107329380 · Cited by 66 · see citing works
Sherif, M. R., Habib, I., Nagshineh, M., et al. (2000) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.824812 · Cited by 66 · see citing works
Optimal Admission Control for Multi-Class of Wireless Adaptive Multimedia Services
Xiao, Y., Chen, P. P., & Wang, Y. (2001) · IEICE Transactions on Communications · Cited by 63 · see citing works
Kwon, T., Park, I., Choi, Y., et al. (1999) · doi:10.1145/313256.313276 · Cited by 42 · see citing works
A Hopfield neural-network-based dynamic channel allocation with handoff channel reservation control
Lázaro, Ó., & Girma, D. (2000) · IEEE Transactions on Vehicular Technology · doi:10.1109/25.892541 · Cited by 37 · see citing works
Scheduling multiprocessor job with resource and timing constraints using neural networks
Huang, Y., & Chen, R. (1999) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.775265 · Cited by 31 · see citing works
Ren, Q., & Ramamurthy, G. (2000) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.824795 · Cited by 27 · see citing works
Improving degradation and fairness for mobile adaptive multimedia wireless networks
Xiao, Y., & Chen, C. L. P. (2001) · Proceedings/Proceedings - International Conference on Computer Communications and Networks · doi:10.1109/icccn.2001.956330 · Cited by 25 · see citing works
Kang, C. G., Ahn, C. W., Jang, K. H., et al. (2000) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.872951 · Cited by 18 · see citing works
Self-citationMisic, J., Chanson, S. T., & Lai, F. S. (2002) · Proceedings/Proceedings - International Conference on Computer Communications and Networks · doi:10.1109/icccn.1997.623322 · Cited by 10 · see citing works
On the proportional fairness of TCP Vegas
Luong, D. D., & Bı́ró, J. (2002) · IEEE Global Telecommunications Conference (GLOBECOM) · doi:10.1109/glocom.2001.965873 · Cited by 7 · see citing works
A MAC protocol for wireless access to ATM networks
Iera, A., Molinaro, A., Marano, S., et al. (2005) · IEEE Vehicular Technology Conference (VTC) · doi:10.1109/vetecf.1999.798597 · Cited by 5 · see citing works
Implicit scheduling algorithm for dynamic slot assignmentin wireless ATM networks
Kang, Kim, & Hwang (1998) · Electronics Letters · doi:10.1049/el:19981590 · Cited by 4 · see citing works
On improving voice quality under dynamic encoding algorithms in ATM networks
Rubin, I., & Chang, K. K. (2002) · Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications · doi:10.1109/scac.1995.523176 · Cited by 3 · see citing works
Girma, D., & Lázaro, Ó. (2001) · Electronics Letters · doi:10.1049/el:20010552 · Cited by 1 · 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
- Authors
- Ahn, C. W., & Ramakrishna, R. S.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- ORCID
- Ahn, C.W.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2004-01
- Volume
- 53
- Issue
- 1
- Pages
- 106-117
- ISSN
- 0018-9545 (ISSN-L)
- OpenAlex
- W2119753046
- Cited by
- 68
- References
- 35
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 2 · 2021: 3 · 2018: 1 · 2016: 4 · 2015: 1 · 2014: 2 · 2013: 3 · 2012: 3
- FWCI
- 7.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Vehicular Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Communication Networks Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Keywords
- quality of service provisioning, wireless multimedia networks, Hopfield neural network, statistical multiplexing gain, resource utilization, resource fairness, blocking probability, dropping probability, congestion, handover, multi-objective optimization
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
