Optimal Access Points Selection Based on Mobility Prediction in Heterogeneous Small Cell Networks

Fu, Wenbin; Li, Xi; Ji, Hong; Zhang, Heli

doi:10.1109/iccchina.2018.8641237

Cited by 3 publications

(2 citation statements)

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“…Limited by the length of the article, some other approaches, such as Markov Renewal Process [53]- [56], Kalman Filter [57], [58] will not be described in detail.…”

Section: ) Bayesian Networkmentioning

confidence: 99%

“…Based on accurate users' movement, BS controller can management next-cell prediction and further encourage mobile users to connect to a better BS. Many introduced prediction approaches in last Section can be used to optimize BS selection [17], [44], [56], [85]. Based on user mobility information, the authors in [89] propose a cell selection algorithm, which first predicts candidate next cells and further chooses the one with maximum SINR as the target cell.…”

Section: ) Handover Managementmentioning

confidence: 99%

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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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“…Limited by the length of the article, some other approaches, such as Markov Renewal Process [53]- [56], Kalman Filter [57], [58] will not be described in detail.…”

Section: ) Bayesian Networkmentioning

confidence: 99%