Minimizing Transmission Costs through Adaptive Marking in Differentiated Services Networks
MMNS '02 Proceedings of the 5th IFIP/IEEE International Conference on Management of Multimedia Networks and Services: Management of Multimedia on the Internet
Reinforcement Learning for Control of Traffic and Access Points in Intelligent Wireless ATM Networks
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Call Admission Control for Multimedia Cellular Networks Using Neuro-dynamic Programming
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
Call admission control in cellular networks: a reinforcement learning solution
International Journal of Network Management
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
Mobile Networks and Applications - Special issue: Recent advances in wireless networking
Assured end-to-end QoS through adaptive marking in multi-domain differentiated services networks
Computer Communications
Cross-layer QoS provisioning for multimedia transmissions in cognitive radio networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
IEEE Journal on Selected Areas in Communications
A general framework for analyzing the optimal call admission control in DS-CDMA cellular network
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
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We solve the adaptive call admission control (CAC) problem in multimedia networks via reinforcement learning (RL). The problem requires that network revenue be maximized while simultaneously meeting quality of service (QoS) constraints that forbid entry into certain states and use of certain actions. We show that RL provides a solution to this constrained semi-Markov decision problem and is able to earn significantly higher revenues than alternative heuristics. Unlike other model-based algorithms, RL does not require the explicit state transition models to solve the decision problems. This feature is very important if one considers large integrated service networks supporting a number of different service types, where the number of states is so large that model-based optimization algorithms are infeasible. Both packet-level and call-level QoS constraints are addressed, and both conservative and aggressive approaches to the QoS constraints are considered. Results are demonstrated on a single link and extended to routing on a multilink network