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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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A supervised-learning-based spatial performance prediction framework for heterogeneous communication networks

  • Mukherjee, Shubhabrata (Department of Computer Science and Electrical Engineering, University of Missouri) ;
  • Choi, Taesang (Electronics Telecommunications Research Institute) ;
  • Islam, Md Tajul (Department of Computer Science and Electrical Engineering, University of Missouri) ;
  • Choi, Baek-Young (Department of Computer Science and Electrical Engineering, University of Missouri) ;
  • Beard, Cory (Department of Computer Science and Electrical Engineering, University of Missouri) ;
  • Won, Seuck Ho (Electronics Telecommunications Research Institute) ;
  • Song, Sejun (Department of Computer Science and Electrical Engineering, University of Missouri)
  • Received : 2020.04.29
  • Accepted : 2020.08.24
  • Published : 2020.11.16
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Abstract

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

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