In this paper, we solve the adaptive call admission control 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 the number of states is so large that model-based optimization algorithms is infeasible. Both packet-level and calllevel QoS constraints are addressed, and both conservative and aggressive approaches to the QoS constraints are considered. Corresponding auth...

This paper examines the application of reinforcement learning to a telecommunications networking problem. The problem requires that revenue be maximized while simultaneously meeting a quality of service constraint that forbids entry into certain states. We present a general solution to this multi-criteria problem that is able to earn significantly higher revenues than alternatives.

In this paper, we solve the call admission control and routing problem in multimedia networks via reinforcement learning (RL). The problem requires that network revenue be maximized while simultaneously meeting quality of service constraints that forbid entry into certain states and use of certain actions. The problem can be formulated as a constrained semi-Markov decision process. We show that RL provides a solution to this problem and is able to earn significantly higher revenues than alternative heuristics.

This paper looks at the general problem of resource allocation in telecommunication networks. It gives an overview of the problem and argues for adaptive methods in the complex telecommunication environment. In particular it discusses a general methodology known as reinforcement learning. The paper presents two examples -- admission control in packet data networks, and battery management for mobile communication.

Tasks such as admission control in ATM and predicting overload conditions in telephone networks require a function that specifies what conditions will result in loss rates exceeding a threshold, p*. This paper considers the formal task of deriving such a classification function based on samples at different conditions. When the size of these samples is small relative to 1/p*, previously proposed methods incorrectly classify conditions that surpass the threshold by orders of magnitude. This paper derives general conditions for consistent and robust classifiers and presents specific methods that meet these conditions. The paper analyzes the methods with respect to asymptotic and finite sample behavior and the results are confirmed using simulated data. I.