Mobility Awareness in Cellular Networks to Support Service Continuity in Vehicular Users

Kuruvatti, Nandish P.; Mallikarjun, Sachinkumar Bavikatti; Schotten, Hans D.

doi:10.1109/icoiact50329.2020.9332125

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Therefore, it seems necessary to develop solutions enabling the global management of edge servers deployed at different levels (infrastructure, UE) and accessible through different RATs and different operators. To achieve this, various solutions appear to be relevant and have started to be explored in this context, notably, SDN [ 137 , 138 ] and AI techniques [ 139 , 140 ].…”

Section: Future Directionsmentioning

confidence: 99%

Multi-Connectivity for 5G Networks and Beyond: A Survey

Sylla

Mendiboure

Maaloul

et al. 2022

Sensors

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To manage a growing number of users and an ever-increasing demand for bandwidth, future 5th Generation (5G) cellular networks will combine different radio access technologies (cellular, satellite, and WiFi, among others) and different types of equipment (pico-cells, femto-cells, small-cells, macro-cells, etc.). Multi-connectivity is an emerging paradigm aiming to leverage this heterogeneous architecture. To achieve this, multi-connectivity proposes to enable UE to simultaneously use component carriers from different and heterogeneous network nodes: base stations, WiFi access points, etc. This could offer many benefits in terms of quality of service, energy efficiency, fairness, mobility, and spectrum and interference management. Therefore, this survey aims to present an overview of multi-connectivity in 5G networks and beyond. To do so, a comprehensive review of existing standards and enabling technologies is proposed. Then, a taxonomy is defined to classify the different elements characterizing multi-connectivity in 5G and future networks. Thereafter, existing research works using multi-connectivity to improve the quality of service, energy efficiency, fairness, mobility management, and spectrum and interference management are analyzed and compared. In addition, lessons common to these different contexts are presented. Finally, open challenges for multi-connectivity in 5G networks and beyond are discussed.

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Section: Future Directionsmentioning

confidence: 99%

Multi-Connectivity for 5G Networks and Beyond: A Survey

Sylla

Mendiboure

Maaloul

et al. 2022

Sensors

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“…The SVM gives a high accuracy rate compared to the DNN and semi-Markov algorithms [3]. [4], [5] and [6] are all ML-based Fig. 1.…”

Section: Introductionmentioning

confidence: 96%

“…Previous work including Kalman Filter-based predictor and Hidden-Markov model approach for the location prediction, pattern-matching algorithm -Hierarchical Location Prediction (HLP) are considered. In [5], the SVM algorithm is used to predict the vehicular user route. Supervised learning takes input variables (X) and output variables (Y ) and uses an algorithm to learn the mapping function from the input to the output Y = f (X).…”

Section: Introductionmentioning

confidence: 99%

Mobility prediction Based on Machine Learning Algorithms

Wang¹,

Qiuheng²,

Partani³

et al. 2021

Preprint

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Nowadays mobile communication is growing fast in the 5G communication industry. With the increasing capacity requirements and requirements for quality of experience, mobility prediction has been widely applied to mobile communication and has becoming one of the key enablers that utilizes historical traffic information to predict future locations of traffic users, Since accurate mobility prediction can help enable efficient radio resource management, assist route planning, guide vehicle dispatching, or mitigate traffic congestion. However, mobility prediction is a challenging problem due to the complicated traffic network. In the past few years, plenty of researches have been done in this area, including Non-Machine-Learning (Non-ML)based and Machine-Learning (ML)-based mobility prediction. In this paper, firstly we introduce the state of the art technologies for mobility prediction. Then, we selected Support Vector Machine (SVM) algorithm, the ML algorithm for practical traffic date training. Lastly, we analyse the simulation results for mobility prediction and introduce a future work plan where mobility prediction will be applied for improving mobile communication.

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