Next-cell Prediction Based on Cell Sequence History and Intra-cell Trajectory

Goodarzi, Meysam; Maletić, Nebojša; Gutiérrez, Jesús; Sark, Vladica; Grass, Eckhard

doi:10.1109/icin.2019.8685910

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…The method uses a long-term trajectory to train a Markov process and uses the short-term trajectory to predict the user’s current possible random behavior. The accuracy of the next location prediction based on this model has been proven to reach more than 70% [ 37 ]. Another location prediction method combining a variable-order Markov model and the spatiotemporal law of the user has been proposed [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

Yan

Chan

et al. 2021

Sensors

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The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

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Section: Related Workmentioning

confidence: 99%

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

Yan

Chan

et al. 2021

Sensors

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“…Based on users' long-term and short-term trajectories, the authors of [86] propose a hybrid cell prediction approach.…”

Section: Hybrid Approachesmentioning

confidence: 99%

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

2020

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Mobility prediction is a powerful tool for network operators to optimize network performance. From cell level, if network operators know the cells to which the users will be connected in advance, wireless resources can be pre-allocated to improve network performance and better user experience can be provided in location-based services. Many next-cell prediction models and methods have been suggested and implemented. This paper is devoted to next-cell prediction (cell level mobility prediction) in cellular networks, and provides a thorough survey of the prediction schemes and applications. Particularly, a two-level classification methodology was proposed and applied. We first divided the prediction schemes into three categories based on the mobility data used for prediction, i.e. Current Movement State based Approaches (CMSA), Historical Movement Pattern based Approaches (HMPA), and HybriD Approaches (HDA). Prediction schemes in each category were further classified based on the used prediction methods. The typical application scenarios were introduced as well, including handover management, resource allocation, etc. Finally, current challenges and potential trends in the near future were further discussed.

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“…Pattern matching prediction schemes in [15,16] search past mobility traces of the node for a matched mobility pattern to predict the future network mobility. The authors in [17] use the cell sequence history to predict the next cell and the trajectory of the mobile node. Machine learning techniques are also used in predicting node's future location in cellular networks [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Prediction of nodes mobility in 3-D space

Al-Hattab

Hamada

2021

IJECE

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<span>Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the three-dimensional space. Accurate mobility prediction leads to an efficient data delivery for real time applications and enables the network to plan for future tasks such as route planning and data transmission in an adequate time and a suitable space. In this paper, we proposed, tested and validated an algorithm that predicts the future mobility of mobile networks in three-dimensional space. The prediction technique uses polynomial regression to model the spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.</span>

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Next-cell Prediction Based on Cell Sequence History and Intra-cell Trajectory

Cited by 11 publications

References 23 publications

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

Prediction of nodes mobility in 3-D space

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