LSTM循环神经网络的高速铁路越区切换算法  

Handover algorithm for a high-speed railway based on the LSTM recurrent neural network

作　　者：陈永[1] 牛凯玉 康婕 CHEN Yong;NIU Kaiyu;KANG Jie(School of Electronics and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,甘肃兰州730070

出　　处：《西安电子科技大学学报》2023年第1期76-84,92,共10页Journal of Xidian University

基　　金：国家自然科学基金(61963023,61841303);兰州交通大学天佑创新团队(TY202003)。

摘　　要：在高速铁路列车运行过程中,为了保持不中断通信,列车需要不断地与基站进行越区切换。越区切换作为LTE-R通信的关键技术,对于保障行车安全至关重要。针对下一代高速铁路无线通信LTE-R系统越区切换算法,因迟滞门限参数固定而导致切换成功率低等问题,提出了一种基于长短期记忆循环神经网络的高速铁路越区切换算法。首先,利用长短期记忆神经网络的记忆特性以及高速铁路越区切换重叠区信号时空相关性的特点,构建了基于长短期记忆循环神经网络的越区切换迟滞门限参数动态预测深度学习网络;其次,通过提出的长短期记忆深度学习模型,对越区切换迟滞参数进行线下训练和线上预测来获取未来时刻的切换门限值,实现了对越区切换迟滞参数的自适应预测,克服了迟滞门限参数固定的缺点;最后,通过仿真实验的结果表明,所提基于长短期记忆循环神经网络的高速铁路越区切换算法较其他比较方法能够有效地提高越区切换的成功率,并降低乒乓切换率的影响。In the process of high-speed railway train operation,in order to maintain uninterrupted communication,the train needs to constantly carry out handover with the base station.As a key technology of LTE-R communication,handover is crucial to ensuring traffic safety.Aiming at the low success rate of handover in the next generation high-speed railway LTE-R wireless communication system due to the fixed hysteresis threshold parameters,a high-speed railway handover algorithm based on the LSTM recurrent neural network is proposed.First,by using the memory characteristics of the LSTM neural network and the temporal and spatial correlation characteristics of high-speed railway handover overlapping area signals,a deep learning network for dynamic prediction of handover hysteresis threshold parameters based on the LSTM recurrent neural network is constructed.Second,through the proposed LSTM deep learning model,the handover hysteresis parameters are trained offline and predicted online to obtain the handover threshold value at the future time,which realizes the adaptive prediction of handover hysteresis parameters during high-speed train driving,and overcomes the disadvantage of fixed hysteresis threshold parameters.Finally,simulation results show that the proposed method can effectively improve the handover success rate and reduce the impact of the ping-pong handover rate compared with the traditional A3 algorithm and other comparison algorithms.Research results provide a certain theoretical reference for high-speed railway traffic safety and LTE-R evolution.

关 键 词：越区切换 铁路长期演进通信系统 长短期记忆循环神经网络 切换预测 高速铁路 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] U285[自动化与计算机技术—计算机科学与技术]