Idle Time Window Prediction in Cellular Networks with Deep Spatiotemporal Modeling

Fang, Luoyang; Cheng, Xiang; Wang, Haonan; Yang, Liuqing

doi:10.1109/jsac.2019.2904367

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…In the graph representation of the network, each vertex has different numbers of adjacent vertices, and it is difficult to hold a convolution kernel with a same size. In recent years, the graph convolutional network (GCN) has been widely concerned, which can realize convolution operation on non-Euclidean structure, effectively learn the spatial characteristic information of nodes, and has been applied in action recognition, traffic network flow prediction, and so on [46][47][48].…”

Section: Spatial Relation Feature Extraction Modelmentioning

confidence: 99%

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

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Accurate and real-time network traffic flow forecast holds an important role for network management. Especially at present, virtual reality (VR), artificial intelligence (AI), vehicle-to-everything (V2X), and other technologies are closely combined through the mobile network, which greatly increases the human-computer interaction activities. At the same time, it requires high-throughput, low delay, and high reliable service guarantee. In order to achieve ondemand real-time high-quality network service, we must accurately grasp the dynamic changes of network traffic. However, due to the increase of client mobility and application behavior diversity, the complexity and dynamics of network traffic in the temporal domain and the spatial domain increase sharply. To accurate capture the spatiotemporal features, we propose the spatial-temporal graph convolution gated recurrent unit (GC-GRU) model, which integrates the graph convolutional network (GCN) and the gated recurrent unit (GRU) together. In this model, the GCN structure could handle the spatial features of traffic flow with network topology, and the GRU is used to further process spatiotemporal features. Experiments show that the GC-GRU model has better prediction performance than other baseline models and can obtain spatial-temporal correlation in traffic lows better.

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Section: Spatial Relation Feature Extraction Modelmentioning

confidence: 99%

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

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“…Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for demand-aware network resource allocation. More example approaches can be found in [118,269,270,272,273,275,[277][278][279].…”

Section: Network Predictionmentioning

confidence: 99%

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

et al. 2021

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This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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“…The weight make BSENet adapt to different tasks. Finally, all the losses are summed up to get the final loss L b for BS b: (14) E. TRAINING AND INFERRING…”

Section: Decodermentioning

confidence: 99%

“…Recently, some studies have tried to explore the spatio-temporal information of BS. With the help of spatio-temporal information, some research has been done, such as mobile traffic prediction [4]- [8], cloud-RAN optimization [9], [10], ultra dense networks optimization [11], improvement of cell-edge users [12], repetitive measurements in LTE [13], and idle time window prediction [14]. There is much research designing algorithm…”

Section: Introductionmentioning

confidence: 99%

BSENet: A Data-Driven Spatio-Temporal Representation Learning for Base Station Embedding

et al. 2020

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Base station (BS) plays a critical role in the wireless network. There has been some research on exploring the spatio-temporal information of BS in different fields. However, these works lack re-usability, which require the new researcher to redo the work of representing the spatio-temporal information. To solve this problem, we propose a neural network model based on autoencoder and representation learning called BSENet to learn embedding of BS based on raw data. The embedding contains spatio-temporal information of BS. In this way, other fields that are related to BS can make use of spatio-temporal information with BSENet embeddings. Besides the spatial information, BSENet can maintain the independence of BS. Moreover, we use bi-directional LSTM to get temporal information and propose a time dropout method to improve the generalization ability. We propose a soft threshold to consider all spatial relations. In addition, we introduce weight to enhance compatibility. We treat the missing states as the inputs to deal with the missing values. The results of clustering show that BSENet embedding is better than other embeddings. In experiments of mobile traffic prediction, BSENet embedding helps a temporal model to improve the Mean Squared Error (MSE) from 0.01463 to 0.01334, which is similar to 0.01302 of a spatio-temporal model. At the same time, the training time increases a little but is still 5.0× faster than spatio-temporal model.

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Idle Time Window Prediction in Cellular Networks with Deep Spatiotemporal Modeling

Cited by 15 publications

References 24 publications

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

BSENet: A Data-Driven Spatio-Temporal Representation Learning for Base Station Embedding

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