상세정보
학술지
A Supervised-learning-based Spatial Performance Prediction Framework for Heterogeneous Communication Networks
- 발행일
- 202010
- 출처
- ETRI Journal, v.42 no.5, pp.686-699
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 20GH1100, 위성과 셀룰러간 유연한 연동을 위한 5G 무선네트워크 기술 개발, 김일규
- 초록
- 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.
- KSP 제안 키워드
- Computational Efficiency, Data-based prediction, Dynamic Resource Allocation(DRA), Learning-based, Linear Discriminant Analysis(LDA), Load balancing, Network performance prediction, Next-generation, Optimizing performance, Performance changes, Prediction framework
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