A Reinforcement Learning Approach to Call Admission and Call Dropping Control in Links with Variable Capacity

作者：Pietrabissa Antonio^*

来源：European Journal of Control, 2011, 17(1): 89-103.

DOI：10.3166/EJC.17.89-103

• Call control
• Reinforcement Learning (RL)
• Markov Decision Processes (MDP)
• communication networks

This paper defines a reinforcement learning (RL) approach to call control algorithms in links with variable capacity supporting multiple classes of service. The novelties of the document are the following: i) the problem is modeled as a constrained Markov decision process (MDP); ii) the constrained MDP is solved via a RL algorithm by using the Lagrangian approach and state aggregation. The proposed approach is capable of controlling class-level quality of service in terms of both blocking and dropping probabilities. Numerical simulations show the effectiveness of the approach.

• 出版日期2011-2