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Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN.

Yanchao Zhao1,2,3, Jie Wu4, Wenzhong Li5,6

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. zhaozola@gmail.com.

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Summary
This summary is machine-generated.

Edge computing enhances mobile networks by moving computation closer to users. This study introduces an efficient, measurement-based method to accurately estimate interference, improving network capacity and reducing measurement time.

Keywords:
cloud-RANedge computinginterference measurementmodelingresource allocation

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Area of Science:

  • Mobile Network Architecture
  • Edge Computing
  • Resource Allocation

Background:

  • Edge computing offers a promising mobile network architecture to address operator challenges and growing user demands.
  • Traditional resource allocation algorithms are becoming feasible with edge computing's increased computational power.
  • Inaccurate interference modeling in sensor networks limits the optimality of resource allocation, necessitating data-driven approaches.

Purpose of the Study:

  • To propose an efficient and intelligent measurement-based solution for estimating inter-node interference in edge computing environments.
  • To improve total network capacity through accurate, measurement data-driven resource allocation.
  • To reduce the time and complexity associated with traditional interference measurement methods.

Main Methods:

  • Developed a measurement-based estimation solution utilizing an accuracy-driven model for dynamic control of measurement and estimation.
  • Employed a link similarity model and a channel derivation model to reduce measurement costs.
  • Implemented measurement scheduling and an accuracy control process to balance accuracy and overhead.

Main Results:

  • Significantly reduced time costs for interference measurement to a linear order of network size compared to exhaustive methods.
  • Achieved guaranteed accuracy through intelligent measurement scheduling and control processes.
  • Demonstrated the efficiency of the proposed solutions through extensive experiments using real data traces.

Conclusions:

  • The proposed measurement-based estimation solution efficiently obtains interference data in edge computing networks.
  • The approach effectively reduces measurement overhead while maintaining accuracy, addressing a key challenge in resource allocation.
  • This method enhances the practical benefits of edge computing by enabling more optimal resource allocation and improved network capacity.