Parameter Adaptation and Situation Awareness of <i>LTE-R</i> Handover for High-Speed Railway Communication
Cheng Wu, Xingqiang Cai, Jie Sheng, Ziwen Tang, Bo Ai, Yiming Wang
Abstract
In the evolution of railway mobile communications from Long Term Evolution for Railway ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LTE-R</i> ) to the future 5th Generation Wireless System ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5G</i> ), the rapid increase in the number of low-power base station nodes along the railway has brought more frequent handovers. The current handover parameter selection mechanism often relies on the on-site measured results in a limited number of discrete scenarios. It cannot deal with the continuous changing characteristics of the high-speed railway mobile communication environment, which leads to a serious lack of accuracy, adaptability and intelligence. This article hopes to construct a parameter-adaptive handover mechanism suitable for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5G</i> in the high-speed railway dedicated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LTE-R</i> communication system. The mechanism first uses the interaction of Temporal-Difference( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TD</i> )-learning-based reinforced agents to obtain high-speed railway handover performance and network performance in different combinations of speeds and handover parameters, and continuously updates the accumulated rewards used to target optimization, obtaining a Discrete TD value cube with closely related handover performance. Further, based on the Discrete TD value cube, we use the approximation function method for the completion of “continuous” situation of handover parameter selection, and construct a continuous TD value cube and the corresponding performance cubes. Our experimental results prove that TD learning agents with function approximation can accurately estimate and predict the handover performance and network performance of state combinations with different speeds and handover parameters, and further show that the handover parameter adaptation mechanism based on the Inference ability can find the optimal handover parameters to improve the handover performance and network performance.