空天异构网络中基于Q学习的切换判决优化算法

Handoff Decision Optimized Algorithm Based on Q-learning Approach for Heterogeneous Networks in Aerospace

小型空中飞行平台在接入空天异构无线网络时,现有切换算法未充分考虑下一时刻网络与用户状态以及用户对不同业务的传输需求。为此,提出一种支持空天异构无线网络的Q学习优化算法。在回报函数中考虑用户体验,将通过综合预测方法获得的当前与下一时刻网络的信干燥比、用户移动速度、网络切换代价、信息传输的时延及网络的拥塞程度作为综合评价参数,同时根据层次分析法确定不同业务类型下的评价参数权值。仿真结果表明,在用户到达率较高、环境干扰强度较强时,相比基于Q学习的切换判决算法,该算法可以有效提高网络切换成功率并降低切换次数,在传输不同类型的业务时,可以提供更优的网络切换策略并降低切换阻塞率。

The existing handoff algorithm lacking of considering status about the next time networks and user status and the user＇s demand for transmitting different service when the small-scale aircraft is connected to the aerospace heterogeneous wireless networks. In order to solve the problem,a Q-learning optimized algorithm is proposed to support the heterogeneous wireless networks. The algorithm introduces user experience into reward function,and uses the comprehensive prediction method to get the current and next networks Signal to Interference and Noise Ratio（ SINR）,user mobility speed,networks handoff cost,information transmission delay and networks congestion degree as comprehensive evaluation parameters. Then,it applies the Analytic Hierarchy Process（ AHP） method to determine the evaluation parameter weight according to the service type difference in transmission networks. Simulation results show that,the proposed algorithm has better preference in improving the successful handoff ratio and reducing the handoff number than the traditional algorithm based on Q-learning,when there are different types of services in the networks,the proposed algorithm can provide better networks handoff strategy and reduce the blocking ratio of handoff.