Abstract:

In this paper, a reinforcement learning based joint session admission control algorithm is proposed to realize the autonomic and distributed joint resource optimization between the heterogeneous radio access technologies (RAT) in a reconfigurable system. By introducing Q-learning into the admission control algorithm and adjusting the strength of the reinforcement signals for different types of sessions considering the inherent characteristics of different RATs, RATs are driven to absorb the suitable traffic for a proper service distribution, which improves the efficiency of resource utilization. The simulation results show that, through the “trial-and-error” on-line learning process, overlapping RATs can converge to the optimized admission control policies that reduce the overall blocking probability while achieve lower handover dropping probability as well as higher revenue.

Key words: reconfigurable, JOSAC, reinforcement learning, Q-learning, distributed, autonomic