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.

中文翻译：

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异构网络中基于Q学习和MADM的WiFi卸载算法

针对蜂窝网络和WiFi网络共存的移动用户场景，提出了一种基于Q学习和MADM（多属性决策）的WiFi卸载算法。马尔可夫模型用于描述网络环境的变化。考虑包括用户吞吐量，终端功耗，用户成本和通信延迟在内的四个属性，以定义反映QoS（服务质量）的用户满意度函数，并使用Q学习对其进行优化。通过MADM中的AHP（层次分析法）和TOPSIS（类似于理想解决方案的订单偏好技术），获得了每个属性与奖励函数之间的内在联系。用户使用Q学习根据当前网络状况和他们自己的卸载历史做出卸载决策，最终使他们的满意度最大化。仿真结果表明，该算法的用户满意度优于传统的WiFi卸载算法。