Paper Info
Title
Next Point-of-Attachment Selection Based on Long Short Term Memory Model in Wireless Networks
Abstract
Existing mobility management systems in cellular networks are ill-equipped to support Ultra-Reliable and Low Latency Communication (URLLC) requirement of next generation prevalent and real time services in dense network deployments due to their reactive approach. Proactive approach is one way to meet the URLLC requirement of these services, where resource assignment and control signaling is completed before the actual user mobility. Successful execution of proactive mobility requires accurate prediction of user next Point of Attachment (PoA) and precise estimation of user mobility instant. This paper adopts supervised deep learning approach to predict the next PoA of user. In particular, a Long Short-Term Memory (LSTM) model is developed for this purpose, which exploits the temporal characteristics of the data. We discuss different design choices of our LSTM model and show their effects on the prediction accuracy. Evaluation results reveal that proper data preprocessing and time-step increment significantly affects the prediction accuracy. The highest accuracy achieved by our model is 91% with shuffled data and stacked LSTM.
Year
DOI
Venue
2020
10.1109/IMCOM48794.2020.9001672
2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM)
Keywords
Field
DocType
Proactive,Mobility management,Deep learning,LSTM
Wireless network,Mobility management,Computer science,Data pre-processing,Long short term memory,Real-time computing,Exploit,Cellular network,Artificial intelligence,Latency (engineering),Deep learning
Conference
ISBN
Citations 
PageRank 
978-1-7281-5454-1
1
0.37
References 
Authors
3
6
Name
Order
Citations
PageRank
Huigyu Yang110.37
Syed M. Raza2188.68
Moonseong Kim314339.75
Duc Tai Le410.71
Van Vi Vo510.37
Hyunseung Choo61364195.25