多路径并行传输协议(multi-path transfer control protocol,MPTCP)是当前在异构网络中执行垂直切换时采用的重要策略之一,良好的拥塞控制策略可降低异构网络垂直切换过程中的网络吞吐量波动问题。文章基于强化学习的思想,构建了面向MPTCP的拥塞控制策略生成机制,并将其应用到电力物联网异构网络垂直切换。主要贡献是:基于MPTCP构建强化学习环境,将异构网络时变属性作为环境要素,MPTCP的拥塞控制策略作为智能体行为策略。智能体和异构网络仿真环境的通过垂直切换的执行实现交互学习,生成最优的拥塞控制策略;基于改进后的MPTCP拥塞控制策略实现异构网络的垂直切换,优化异构网络的网络切换进程。仿真及实际场景的测试结果显示,所提方法在电力物联网异构网络切换过程中避免了数据断点,有效降低了异构网络切换过程中的吞吐量波动情况。
In order to ensure the uninterrupted communication between high-speed train and base station,driving safety and satisfying online experience of passengers,a dual-link switching algorithm based on CNN-WaveNet decision parameter multi-step prediction model is proposed to establish a two-hop relay communication system model between the high-speed train and the base station.Firstly,the switching algorithm uses convolution neural network(CNN)to extract the time sequence characteristics of decision parameters.Then,it learns the mapping relationship between feature information and decision parameters based on WaveNet and combining with rolling prediction method to realize multi-step prediction of decision parameters.Finally,dual-antenna communication mode is adopted to realize dual-link communication.The simulation results show that the proposed handover algorithm can improve handover trigger rate and handover success rate.
文章提出一种基于XGBoost算法的自适应网络切换方法,优化工业物联网(Industrial Internet of Things,IIoT)环境中Wi-Fi与5G网络的切换效率。通过XGBoost模型深度学习历史网络性能数据和环境参数,智能预测最优网络切换时机和目标网络类型。该方法实现了动态网络选择,并结合动态缓存系统利用历史数据优化决策,提高了切换效率和响应速度。引入的回滚检查机制确保在网络性能下降或切换失败时能够迅速恢复到稳定状态,保障通信质量。实验评估表明,该方法在切换成功率、平均延迟和系统开销方面表现优异,为提高IIoT设备的通信性能提供了有效解决方案。