TY - GEN
T1 - Trajectory Prediction with Recurrent Neural Networks for Predictive Resource Allocation
AU - Zhang, Wenjing
AU - Liu, Yuan
AU - Liu, Tingting
AU - Yang, Chenyang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/2/2
Y1 - 2019/2/2
N2 - Trajectory prediction of mobile users plays a key role in making the plan for predictive radio resource allocation, i.e., determining which base stations alongside the trajectory of a user serve the user with how much resources. Predictive resource allocation in existing literature requires the prediction with second-level resolution and minute-level horizon. However, the trajectories predicted with existing methods are either too coarse-grained or with too short-horizon. In this paper, we strive to filling this gap by developing a recurrent neural network based trajectory prediction method. With proper network architecture and output structure, the proposed method can provide high-accuracy prediction with horizon of one minute. We investigate the performance of large-scale channel prediction with a perfect radio map. We also provide the statistics of the prediction errors for trajectory and large-scale channel gains, which is useful for the robust optimization of predictive resource allocation.
AB - Trajectory prediction of mobile users plays a key role in making the plan for predictive radio resource allocation, i.e., determining which base stations alongside the trajectory of a user serve the user with how much resources. Predictive resource allocation in existing literature requires the prediction with second-level resolution and minute-level horizon. However, the trajectories predicted with existing methods are either too coarse-grained or with too short-horizon. In this paper, we strive to filling this gap by developing a recurrent neural network based trajectory prediction method. With proper network architecture and output structure, the proposed method can provide high-accuracy prediction with horizon of one minute. We investigate the performance of large-scale channel prediction with a perfect radio map. We also provide the statistics of the prediction errors for trajectory and large-scale channel gains, which is useful for the robust optimization of predictive resource allocation.
KW - Large-scale channel gain prediction
KW - Predictive resource allocation
KW - Recurrent neural networks
KW - Trajectory prediction
UR - https://www.scopus.com/pages/publications/85063288999
U2 - 10.1109/ICSP.2018.8652460
DO - 10.1109/ICSP.2018.8652460
M3 - 会议稿件
AN - SCOPUS:85063288999
T3 - International Conference on Signal Processing Proceedings, ICSP
SP - 634
EP - 639
BT - ICSP 2018 - 2018 14th IEEE International Conference on Signal Processing, Proceedings
A2 - Baozong, Yuan
A2 - Qiuqi, Ruan
A2 - Yao, Zhao
A2 - Gaoyun, An
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Signal Processing, ICSP 2018
Y2 - 12 August 2018 through 16 August 2018
ER -