상세정보
학술지
Path Loss Model Based on Machine Learning Using Multi-Dimensional Gaussian Process Regression
- 발행일
- 202211
- 출처
- IEEE Access, v.10, pp.115061-115073
- ISSN
- 2169-3536
- 출판사
- IEEE
- 협약과제
- 22HH2700, 선제적 주파수 이용을 위한 시·공간적 스펙트럼 엔지니어링 기술 개발, 정영준
- 초록
- For beyond fifth-generation (5G) and future wireless communications, spatial consistency that represents the correlation between propagation channel characteristics in close proximity has become one of the major issues in channel modeling to describe channels more realistically in emerging scenarios such as device-to-device (D2D). In this paper, we propose a novel path loss model based on multi-dimensional Gaussian process regression (GPR) that gives spatial consistency to channels in propagation environment by predicting local shadow fading while fitting large-scale path loss from measured data. The proposed model has a special structure consisting of a radial mean function and a local shadow fading term. In contrast to the log-distance path loss model and other regression-based approaches, the special structure of the proposed model provides good spatial consistency. Moreover, since the proposed model is based on GPR, it provides the uncertainty of the predicted path loss. We validate the performance of the proposed model in terms of prediction accuracy with the measurement datasets from two different indoor environments. Our experiments show that the proposed model predicts better than the log-distance path loss model, especially when spatial correlation gets more significant. The proposed model can be also used to simulate path loss in a general environment after training the measurement data.
- KSP 제안 키워드
- Channel Characteristics, Channel modeling, Device to Device(D2D), Fifth Generation(5G), Gaussian process regression, Indoor Environment, Prediction accuracy, Propagation Channel, Proposed model, Regression-based, Spatial consistency
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
