CTM-Based Reinforcement Learning Strategy for Optimal Heterogeneous Wireless Network Selection
Kittisak Kittiwaytang, Pitipong Chanloha, Chaodit Aswakul · 2010
This paper proposes the framework to find the optimal selection of heterogeneous wireless network. Reinforcement learning (RL) model is used to find the best strategy to maximise the reward function expressed in terms of call blocking and call dropping probabilities. The reward-evaluation model is based on the well-established macroscopic cell transmission model (CTM), which has the advantage in computational efficiency. CTM has thus been integrated well with RL in the herein developed optimisation framework. The proposed framework has been evaluated in three different scenarios which are the changes of bandwidth, stochastic incoming demands and unpredictable network problems. The results show that CTM-based RL algorithm can lead to the optimal solutions in all the tested scenarios.