Efficient Handover in LTE-A by Using Mobility Pattern History and User Trajectory Prediction

Ahmad, Rami; Sundararajan, Elankovan; Othman, Nor Effendy; Ismail, Mahamod

doi:10.1007/s13369-017-3037-6

Cited by 21 publications

(10 citation statements)

References 28 publications

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“…Ahmad et al [113] proposed a handover scheme that makes use of an ME's historical mobility pattern. When an ME is ready for handover, it first looks into its mobility history to select a target cell.…”

Section: B Security Schemes For Horizontal Handovermentioning

confidence: 99%

Is 5G Handover Secure and Private? A Survey

Zhao

Yan

Wang

et al. 2021

IEEE Internet Things J.

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Section: B Security Schemes For Horizontal Handovermentioning

confidence: 99%

Is 5G Handover Secure and Private? A Survey

Zhao

Yan

Wang

et al. 2021

IEEE Internet Things J.

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“…A hybrid scheme based on handover history table and realtime GPS for Markov process prediction is described in [84]. In [85], the authors also introduce hybrid schemes. The next cell is determined by matching user's current path from the historical connection patterns.…”

Section: Hybrid Approachesmentioning

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%

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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“…Therefore, in this case, server-side, dynamic, link-aware adaptation occurs. We also took the handover into consideration, as we know that there is no soft handover in LTE, and it only supports hard handover, which drastically decreases the bandwidth [39][40][41]. At the time of handover, the server-side adaptation occurs from high-resolution to low-resolution video streaming to achieve better QoE of video monitoring.…”

Section: Proposed Architecturementioning

confidence: 99%

SCRAS Server-Based Crosslayer Rate-Adaptive Video Streaming over 4G-LTE for UAV-Based Surveillance Applications

et al. 2019

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This research focuses on intelligent unmanned aerial vehicle (UAV)-based, real-time video surveillance to ensure better monitoring and security of remote locations over 4G-LTE cellular networks by maximizing end-user quality of experience (QoE). We propose a novel server-based crosslayer rate-adaptive scheme (SCRAS) for real-time video surveillance over 4G-LTE networks using UAVs. Our key contributions are: (1) In SCRAS, mobile UAVs having preprogrammed flight co-ordinates act as servers, streaming real-time video towards a remote client; (2) server-side video rate adaptation occurs in 4G-LTE based on the physical characteristics of the received signal conditions due to variations in the wireless channel and handovers; (3) SCRAS is fully automated and independent of client assistance for rate adaptation, as it is intended for real-time, mission-critical surveillance applications; (4) SCRAS ensures that during rate adaptation, the current video frame should not be damaged by completing the current group of packet (GoP) before adaptation. Our simulations in NS3 provide credible evidence that SCRAS outperforms recently proposed schemes in providing better QoE for real-time, rate-adaptive, video surveillance over 4G-LTE under varying channel quality and frequent handovers that occur during flight by UAVs.

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Efficient Handover in LTE-A by Using Mobility Pattern History and User Trajectory Prediction

Cited by 21 publications

References 28 publications

Is 5G Handover Secure and Private? A Survey

Is 5G Handover Secure and Private? A Survey

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

SCRAS Server-Based Crosslayer Rate-Adaptive Video Streaming over 4G-LTE for UAV-Based Surveillance Applications

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