Application of Neural Networks to Adaptive Control of Nonlinear Systems
Application of Neural Networks to Adaptive Control of Nonlinear Systems
An Adaptive Location-Aware MAC Protocol for Multichannel Multihop Ad-Hoc Networks
NETWORKING '02 Proceedings of the Second International IFIP-TC6 Networking Conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; and Mobile and Wireless Communications
Comparison of routing metrics for static multi-hop wireless networks
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Wireless mesh networks: a survey
Computer Networks and ISDN Systems
Fairly adjusted multimode dynamic guard bandwidth admission control over CDMA systems
IEEE Journal on Selected Areas in Communications
Constraint optimization in call admission control domain with a NeuroEvolution algorithm
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
A QoS Framework with Traffic Request in Wireless Mesh Network
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Performance analysis of two-tier wireless mesh networks for achieving delay minimisation
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
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This paper focuses on integrating connection-level and packet-level QoS controls over wireless mesh network (WMN) to support applications with diverse QoS performance requirements. At the connection-level, the dynamic guard based prioritized connection admission control (DG-PCAC) provides prioritized admission with relative connection blocking probabilities and end-to-end deterministic minimum bandwidth allocation guarantees. DG-PCAC is enabled by dynamic guard based logical link configuration controls (LCCs), which provides relative differentiated capacity limits for prioritized admission classes. At the packet-level, the optimal rate delay scheduler (ORDS) dynamically allocates link bandwidth to the admitted flows of prioritized traffic classes; with the objective to minimize deviation from relative delay targets with minimum bandwidth guarantees according to traffic classes. Two realizations of the ORDS are presented, namely optimal scheduling policy via dynamic programming (DP) algorithm, and neural network (NN) based heuristic to alleviate computational complexity. Performance results show that the DG-PCAC enables consistent performance guarantees under non-stationary arrivals of connection requests. Performance results also show that the performance of the NN based scheduling heuristic approaches to that of the DP based optimal ORDS scheme.