A New Framework for Multi-Hop ABS-Assisted 5G-Networks With Users’ Mobility Prediction

Chaalal, Elhadja; Senouci, Sidi‐Mohammed; Reynaud, Laurent

doi:10.1109/tvt.2022.3149711

Cited by 13 publications

(10 citation statements)

References 47 publications

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“…It enhances the prediction accuracy by implementing the attention block in the encoder at the cost of elevated computation. Another work exploits the encoder-decoder architecture in Transformer network model to predict users mobility which is used as an input for maximizing the coverage of Ariel Base Stations by optimizing their positioning [45]. However, performance of these models in terms of prediction error and accuracy is still not sufficiently reliable and consistent to warrant their inclusion in live wireless networks.…”

Section: Multi-output Approach For Multi-step Predictionmentioning

confidence: 99%

“…For example, AP ID 3 is converted into vector [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] and 5 into vector [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]. This representation of elements is adapted for the comparison models, GRU-Attention (GRU-ATTN) model [44], and Transformer Network (TN) model [45].…”

Section: B Data Collection and Preprocessingmentioning

confidence: 99%

“…To this end, outright accuracy results of three, five, and seven steps ahead predictions for SFI model and ED model are depicted in Fig. 8 as a function of input sequence length, and compared against two stateof-the-art models GRU-ATTN [44], and TN [45]. For the comprehensive analyses, the results of CMD and OMD are separately illustrated in Figs.…”

Section: Epochmentioning

confidence: 99%

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Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead

Yang

Raza

Kim

et al. 2023

IEEE Trans. Cogn. Commun. Netw.

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Importance of user mobility has rapidly increased in 5G due to reduced cell sizes, management of Multi-access Edge Computing (MEC), and ultra-low latency services. Reactive nature of existing management systems is a bottleneck, and it can be solved by building proactive systems that exploit temporal characteristics of time-series mobility data to predict long-term user movement (i.e., path). However, user mobile path prediction with useable accuracy is a challenging task, particularly for lengthy target trajectories. This paper adopts general approaches to propose two models for predicting mobile path with high accuracy.Step Forward Iteration (SFI) model is based on recursive approach, whereas Encoder-Decoder (ED) model follows multi-output approach, and both the models use Long-Short Term Memory (LSTM) as the learning unit. Training and testing of these models is done on mobility datasets from the wireless network of Pangyo ICT Research Center, Korea and one of the Korean mobile operators. The experiment results show viability of the proposed models for leveraging mobile network management, as they outperform state-of-the-art GRU with attention (GRU-ATTN) and Transformer Network (TN) models. The highest prediction accuracies achieved for 3, 5, and 7 steps of target sequences (i.e., predicted mobile path) in the campus dataset are 96%, 90%, and 87%, respectively.

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Section: Multi-output Approach For Multi-step Predictionmentioning

confidence: 99%

Section: B Data Collection and Preprocessingmentioning

confidence: 99%

Section: Epochmentioning

confidence: 99%

See 1 more Smart Citation

Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead

Yang

Raza

Kim

et al. 2023

IEEE Trans. Cogn. Commun. Netw.

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show abstract

“…The number of times that the learning algorithm will work through the entire training dataset results of three, five, and seven steps ahead predictions for SFI model and ED model are depicted in Fig. 8 as a function of input sequence length, and compared against two stateof-the-art models GRU-ATTN [44], and TN [45]. For the comprehensive analyses, the results of CMD and OMD are separately illustrated in Figs.…”

Section: Epochmentioning

confidence: 99%

Deep Mobile Path Prediction with Shift-and-Join and Carry-Ahead

YANG¹,

Raza²,

Choo³

et al. 2022

Preprint

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<p>Mobile user path prediction is a prerequisite for mobility-aware efficient task offloading in Multi-access Edge Computing (MEC), ultra-low latency services, and many other resource management operations. This manuscript addresses the mobile user path prediction by proposing two Deep Learning (DL) driven models that use Long Short-Term Memory (LSTM) to process time-series network data. The training and evaluation of the proposed models are done using our collected and preprocessed dataset which has been made a publicly learnable resource. </p>

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“…However, despite the potential features of UmBS, its deployment in real-world scenarios faces challenges too. The main challenges include the following: estimation of ground UEs position information [ 6 , 7 ], association of UEs to their serving UmBS [ 8 ], association of the UmBS to the core network [ 9 ], resource allocation [ 10 , 11 ], channel characterization [ 12 ], energy optimization [ 13 ], and trajectory optimization [ 14 , 15 ]. In particular, two issues are major and significantly affect the design of the UmBS deployment algorithm.…”

Section: Introductionmentioning

confidence: 99%

Q-learning-based UAV-mounted base station positioning in a disaster scenario for connectivity to the users located at unknown positions

Mandloi

Arya

2023

J Supercomput

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Due to its flexibility, cost-effectiveness, and quick deployment abilities, unmanned aerial vehicle-mounted base station (UmBS) deployment is a promising approach for restoring wireless services in areas devastated by natural disasters such as floods, thunderstorms, and tsunami strikes. However, the biggest challenges in the deployment process of UmBS are ground user equipment’s (UE’s) position information, UmBS transmit power optimization, and UE-UmBS association. In this article, we propose Localization of ground UEs and their Association with the UmBS (LUAU), an approach that ensures localization of ground UEs and energy-efficient deployment of UmBSs. Unlike existing studies that proposed their work based on the known UEs positional information, we first propose a three-dimensional range-based localization approach (3D-RBL) to estimate the position information of the ground UEs. Subsequently, an optimization problem is formulated to maximize the UE’s mean data rate by optimizing the UmBS transmit power and deployment locations while taking the interference from the surrounding UmBSs into consideration. To achieve the goal of the optimization problem, we utilize the exploration and exploitation abilities of the Q-learning framework. Simulation results demonstrate that the proposed approach outperforms two benchmark schemes in terms of the UE’s mean data rate and outage percentage.

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A New Framework for Multi-Hop ABS-Assisted 5G-Networks With Users’ Mobility Prediction

Cited by 13 publications

References 47 publications

Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead

Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead

Deep Mobile Path Prediction with Shift-and-Join and Carry-Ahead

Q-learning-based UAV-mounted base station positioning in a disaster scenario for connectivity to the users located at unknown positions

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