WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks

Abstract

This paper proposes a WiFi offloading algorithm based on Q-learning and MADM (multiattribute decision making) in heterogeneous networks for a mobile user scenario where cellular networks and WiFi networks coexist. The Markov model is used to describe the changes of the network environment. Four attributes including user throughput, terminal power consumption, user cost, and communication delay are considered to define the user satisfaction function reflecting QoS (Quality of Service), and Q-learning is used to optimize it. Through AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) in MADM, the intrinsic connection between each attribute and the reward function is obtained. The user uses Q-learning to make offloading decisions based on current network conditions and their own offloading history, ultimately maximizing their satisfaction. The simulation results show that the user satisfaction of the proposed algorithm is better than the traditional WiFi offloading algorithm.

QAAs: QoS provisioned artificial intelligence framework for AP selection in next-generation wireless networks

“…In [22], authors introduced a machine learning based context aware framework that relies on multi-parametric modelling and optimises the association decision for the sake of optimal resource utilisation. Sun and Zhu [23] adopted a hybrid WiFi offloading algorithm which exploits MADM method to compute the reward function and Q-learning to decide optimal offloading decision in order to maximise the user satisfaction. But this Q-learning based model fails to handle the enormous state space caused by the mobile station roaming in the time varying WLAN environment.…”