Vivisecting mobility management in 5G cellular networks

Hassan, Ahmad; Narayanan, Arvind; Zhang, Anlan; Ye, Wei; Zhu, Ruiyang; Jin, Shuowei; Carpenter, Jason; Mao, Zeyu; Qian, Feng

doi:10.1145/3544216.3544217

Cited by 28 publications

(2 citation statements)

References 31 publications

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“…Further, unlike [53], our work focuses on 5G mid-band. Other works aim at understanding throughput predictability at high-bands [52], physical layer latency [25], mobility management [36], power consumption [54,66] as well as performance implications to specific applications like video streaming [54,59], or to the broader application ecosystem from the user [48] and carrier perspective [57].…”

Section: Related Workmentioning

confidence: 99%

An In-Depth Measurement Analysis of 5G mmWave PHY Latency and Its Impact on End-to-End Delay

Fezeu

Ramadan

et al. 2023

Lecture Notes in Computer Science

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Fifth Generation (5G) mobile networks mark a significant shift from previous generations of networks. By introducing a flexible design, 5G networks support highly diverse application requirements. Currently, the landscape of previous measurement studies does not shed light on 5G network configuration and the inherent implications to application performance. In this paper, we precisely fill this gap and report our in-depth multi-country measurement study on 5G deployed at mid-bands. This is the common playground for U.S. and European carriers. Our findings reveal key aspects on how carriers configure their network, including spectrum utilization, frame configuration, resource allocation and their implication on the application performance.

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Section: Related Workmentioning

confidence: 99%

An In-Depth Measurement Analysis of 5G mmWave PHY Latency and Its Impact on End-to-End Delay

Fezeu

Ramadan

et al. 2023

Lecture Notes in Computer Science

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“…In this work, we demonstrate how Long Term Evolution (LTE) CSI can be explored for proximity estimation with both data-driven and model-based approaches independently. Our solutions capitalize on spatial correlation, an attribute present in all existing wireless channels [34] and even more significant in dense 5G [35] and future 6G networks. Thus, our approaches are expected to outperform current results when deployed in future networks.…”

Section: Introductionmentioning

confidence: 99%

CSI-Based Proximity Estimation: Data-Driven and Model-Based Approaches

Bezerra,

Kouzayha,

Elsawy

et al. 2024

IEEE Open J. Commun. Soc.

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Proximity estimation forms the backbone of contact tracing solutions, as quarantining potentially infected individuals is essential for controlling the spread of epidemics. It is also essential for device discovery in Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, which will be critical in future 6G networks. Despite the widespread coverage of cellular networks, no previous work has evaluated cellular-based proximity estimation in an experimental setting. In this paper, we collect Channel State Information (CSI) measurements from an actual cellular network and utilize them to train and evaluate two proposed solutions. Capitalizing on CSI spatial correlation, we propose a data-driven method to classify devices based on their respective proximity. The proposed neural network has an accuracy of 91.18% when classifying devices as within 5 meters from one another or not, from only 10 seconds of CSI measurements. This method is complemented by a model-based approach that provides a solid theoretical model for estimating proximity. The model-based approach uses Bayesian inference of the conditional distribution of power correlation across devices, assuming spatially-correlated shadowing and stochastic geometry tools. The proposed Bayesian regressor fits our dataset better than the standard exponentially-decaying correlation model, reaching a 2.8× lower RMSE while requiring fine-tuning of only two more parameters. A Bayesian classifier is also proposed and reached a 91.67% accuracy on the binary case while also outperforming the data-driven model significantly at higher distances.INDEX TERMS Bayesian methods, cellular networks, channel models, channel state information, machine learning, proximity detection.

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