Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning
In recent years, long term evolution for railway (LTE-R) has been a promising technology to meet the growing demand for railway wireless communication. To realize the active maintenance of LTE-R base station, it is of great significance to precisely predict the communication quality (CQ) of LTE-R ba...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9095336/ |
| _version_ | 1818433535237685248 |
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| author | Jiantao Qu Feng Liu Yuxiang Ma Jiaming Fan |
| author_facet | Jiantao Qu Feng Liu Yuxiang Ma Jiaming Fan |
| author_sort | Jiantao Qu |
| collection | DOAJ |
| description | In recent years, long term evolution for railway (LTE-R) has been a promising technology to meet the growing demand for railway wireless communication. To realize the active maintenance of LTE-R base station, it is of great significance to precisely predict the communication quality (CQ) of LTE-R base station. Given that the existing LTE CQ prediction methods can not support the active maintenance of LTE-R base station. Furthermore, the LTE-R base station has its unique characteristics in time relationship and regional impact, one of the most challenging problems is to effectively integrate the temporal and spatial information to improve the effect of CQ prediction. To solve the above problems, we choose daily evolved radio access bearer (E-RAB) abnormal release ratio as the CQ indicator, and propose a new deep learning-based CQ prediction approach for LTE-R. Considering the influence of adjacent base stations, this method conducts temporal-spatial collaborative prediction on multivariate time series collected from the CQ data of these stations. First, to eliminate the negative effect of redundant variables, a new variable filter method based on max-relevance, and min-redundancy (MRMR) criterion and binary particle swarm optimization (BPSO) is proposed to select a variable set from the CQ data of related base stations. Second, a new recurrent convolutional neural network (RCNN) model with a self-attention mechanism is proposed to extract temporal-spatial features from the selected variable set. With these features, we build a collaborative prediction model for CQ prediction. Experimental results on real-world LTE-R CQ datasets demonstrate the superiority of the proposed method in CQ prediction. |
| first_indexed | 2024-12-14T16:22:38Z |
| format | Article |
| id | doaj.art-c17d6a0bf5624317a2e6c0b300327827 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:22:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c17d6a0bf5624317a2e6c0b3003278272022-12-21T22:54:46ZengIEEEIEEE Access2169-35362020-01-018948179483210.1109/ACCESS.2020.29954789095336Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep LearningJiantao Qu0https://orcid.org/0000-0002-8664-2236Feng Liu1https://orcid.org/0000-0002-4554-8307Yuxiang Ma2https://orcid.org/0000-0002-6131-2599Jiaming Fan3https://orcid.org/0000-0002-3273-0863School of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaIn recent years, long term evolution for railway (LTE-R) has been a promising technology to meet the growing demand for railway wireless communication. To realize the active maintenance of LTE-R base station, it is of great significance to precisely predict the communication quality (CQ) of LTE-R base station. Given that the existing LTE CQ prediction methods can not support the active maintenance of LTE-R base station. Furthermore, the LTE-R base station has its unique characteristics in time relationship and regional impact, one of the most challenging problems is to effectively integrate the temporal and spatial information to improve the effect of CQ prediction. To solve the above problems, we choose daily evolved radio access bearer (E-RAB) abnormal release ratio as the CQ indicator, and propose a new deep learning-based CQ prediction approach for LTE-R. Considering the influence of adjacent base stations, this method conducts temporal-spatial collaborative prediction on multivariate time series collected from the CQ data of these stations. First, to eliminate the negative effect of redundant variables, a new variable filter method based on max-relevance, and min-redundancy (MRMR) criterion and binary particle swarm optimization (BPSO) is proposed to select a variable set from the CQ data of related base stations. Second, a new recurrent convolutional neural network (RCNN) model with a self-attention mechanism is proposed to extract temporal-spatial features from the selected variable set. With these features, we build a collaborative prediction model for CQ prediction. Experimental results on real-world LTE-R CQ datasets demonstrate the superiority of the proposed method in CQ prediction.https://ieeexplore.ieee.org/document/9095336/Time series forecastingdeep learningLTE-Rcommunication quality prediction |
| spellingShingle | Jiantao Qu Feng Liu Yuxiang Ma Jiaming Fan Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning IEEE Access Time series forecasting deep learning LTE-R communication quality prediction |
| title | Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning |
| title_full | Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning |
| title_fullStr | Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning |
| title_full_unstemmed | Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning |
| title_short | Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning |
| title_sort | temporal spatial collaborative prediction for lte r communication quality based on deep learning |
| topic | Time series forecasting deep learning LTE-R communication quality prediction |
| url | https://ieeexplore.ieee.org/document/9095336/ |
| work_keys_str_mv | AT jiantaoqu temporalspatialcollaborativepredictionforltercommunicationqualitybasedondeeplearning AT fengliu temporalspatialcollaborativepredictionforltercommunicationqualitybasedondeeplearning AT yuxiangma temporalspatialcollaborativepredictionforltercommunicationqualitybasedondeeplearning AT jiamingfan temporalspatialcollaborativepredictionforltercommunicationqualitybasedondeeplearning |