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Journal Article
A supervised‐learning‐based spatial performance prediction framework for heterogeneous communication networks
- Authors
- Shubhabrata Mukherjee, Taesang Choi, Md Tajul Islam, Baek-Young Choi, Cory Beard, Seuck Ho Won, Sejun Song
- Issue Date
- 2020-10
- Citation
- ETRI Journal, v.42, no.5, pp.686-699
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Computational Efficiency, Data-based prediction, Dynamic Resource Allocation(DRA), Learning-based, Load balancing, Network Management, Network functions, Network performance prediction, Next-generation, Optimizing performance, Performance changes
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
