Machine Learning Based Session Drop Prediction in LTE Networks and Its SON Aspects

Daróczy, Bálint; Vaderna, Péter; Benczúr, András A.

doi:10.1109/vtcspring.2015.7145925

Cited by 18 publications

(12 citation statements)

References 5 publications

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“…In this regard, the idea of applying ML to this function is not new. In [184] the authors study the benefits of using ML to root-cause analysis of session drops, as well as drop prediction for individual sessions. They present an offline Adaboost and SVM method to create a predictor, which is in charge of eliminating/mitigating the session drops by using real LTE data.…”

Section: ) Son Conflicts Coordinationmentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

172

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In this paper, we provide an analysis of selforganized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

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Section: ) Son Conflicts Coordinationmentioning

confidence: 99%

From 4G to 5G: Self-organized network management meets machine learning

Moysen

Giupponi

2018

Computer Communications

172

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“…In [10] authors process data from real-time reporting of sessions for network optimization. In order to predict packet drops, before the end of the session, machine learning was used on o ine LTE data.…”

Section: Managing Interference By Using Machine Learning Algorithmsmentioning

confidence: 99%

“…In [10] and [18], authors use historical data and libSVM to create models which predict interference but they did not apply their model to the network to see how it can improve performance metrics. In [31] and [17] they apply their model in the network but they did not use historical data.…”

Section: Managing Interference By Using Machine Learning Algorithmsmentioning

confidence: 99%

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Mining Historical Data towards Interference Management in Wireless SDNs

Karimi

Krishnamurthy

Joshi

et al. 2017

Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks

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WiFi networks are o en planned to reduce interference through planning, macroscopic self-organization (e.g. channel switching) or network management. In this paper, we explore the use of historical data to automatically predict tra c bo lenecks and make rapid decisions in a wireless (WiFi-like) network on a smaller scale. is is now possible with so ware de ned networks (SDN), whose controllers can have a global view of tra c ows in a network. Models such as classi cation trees can be used to quickly make decisions on how to manage network resources based on the quality needs, service level agreement or other criteria provided by a network administrator. e objective of this paper is to use data generated by simulation tools to see if such classi cation models can be developed and to evaluate their e cacy. For this purpose, extensive simulation data were collected and data mining techniques were then used to develop QoS prediction trees. Such trees can predict the maximum delay that results due to speci c tra c situations with speci c parameters. We evaluated these decision/classi cation trees by placing them in an SDN controller. OpenFlow cannot directly provide the necessary information for managing wireless networks so we used POX messenger to set up an agent on each AP for adjusting the network. Finally we explored the possibility of updating the tree using feedback that the controller receives from hosts. Our results show that such trees are e ective and can be used to manage the network and decrease maximum packet delay.

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“…Presently, there are few studies concerning CQ prediction for railway wireless communication, no matter GSM-R or LTE-R. However, in other LTE application scenarios, thanks to the detailed, frequent, high-granularity, real-time reporting of LTE, we can use data-driven technology to further analyze and process the data [2]. There are some researches on CQ prediction in other LTE application scenarios [2]- [6].…”

Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

Liu

et al. 2020

IEEE Access

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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.

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Machine Learning Based Session Drop Prediction in LTE Networks and Its SON Aspects

Cited by 18 publications

References 5 publications

From 4G to 5G: Self-organized network management meets machine learning

From 4G to 5G: Self-organized network management meets machine learning

Mining Historical Data towards Interference Management in Wireless SDNs

Temporal-Spatial Collaborative Prediction for LTE-R Communication Quality Based on Deep Learning

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