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Related Experiment Video

Updated: Jul 6, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Flow-level performance and user mobility in wireless data networks.

Sem Borst1

  • 1Bell Labs, Alcatel-Lucent, Murray Hill, NJ 07974, USA. sem@research.bell-labs.com

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|March 8, 2008
PubMed
Summary
This summary is machine-generated.

Channel variations in wireless networks impact transmission rates. This study shows user mobility enhances network capacity, even with local fair-sharing, by analyzing flow-level stability and performance bounds.

Related Experiment Videos

Last Updated: Jul 6, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Area of Science:

  • Wireless Communication Networks
  • Network Performance Analysis
  • Queueing Theory

Background:

  • Wireless channel conditions vary significantly in space and time, affecting transmission rates.
  • Existing research on channel-aware scheduling primarily focuses on packet-level performance with static user configurations.

Purpose of the Study:

  • To analyze flow-level performance implications for dynamic user populations in wireless networks.
  • To investigate the impact of slower time-scale variations and user mobility on network throughput.
  • To establish stability conditions and performance bounds for various scheduling strategies.

Main Methods:

  • Derivation of necessary and sufficient conditions for flow-level stability.
  • Evaluation of proportional fair scheduling using a processor-sharing model with state-dependent service rates.
  • Analysis of fluid and quasi-stationary regimes to derive performance bounds.
  • Modeling of multi-base station networks with user mobility and session handoffs.

Main Results:

  • Identified simple necessary conditions for flow-level stability, proving them near-sufficient for utility-based scheduling.
  • Demonstrated that user mobility increases network capacity, irrespective of global optimality or local fair-sharing.
  • Established explicit, insensitive performance bounds using fluid and quasi-stationary regimes for slower time-scale variations.

Conclusions:

  • Flow-level stability conditions are crucial for understanding dynamic wireless network performance.
  • User mobility is a significant factor in enhancing overall network capacity.
  • The proposed analytical frameworks provide valuable insights into performance bounds and scheduling strategies in dynamic wireless environments.