Hybrid Markov Models Used for Path Prediction

Path prediction is an important issue in QoS of wireless networks. The paper points out problems in some existed path prediction schemes, especially the state space expansion problem in order-k Markov predictor. And it firstly proposes a step-k Markov model and validates its feasibility. Secondly, a hybrid Markov predictor model and its improved models are put forward based on the step-k Markov model. Because of the order-2 Markov model´s best performance in order-k Markov models, the Hybrid Markov model takes the order-2 Markov model as its target. The state space´s complexity of the Hybrid Markov Model is 0(N) while the order-2 Markov model is O(N²). And the memory demand of the hybrid Markov model is O(N²) while Order-2 Markov model is O(N³). Finally, it is proved that the hybrid Markov predictor can get close performance with order-2 Markov predictor at much lower expense by conditional entropy analysis and user mobility data analysis. Also it can alleviate the zero probability problem in order-k Markov model to some extent. The hybrid Markov predictor is more practical than order-k Markov predictors under WLAN.