지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.15 No.2
- 발행연도
- 2021.6
- 수록면
- 84 - 95 (12page)
- DOI
- 10.5626/JCSE.2021.15.2.84
이용수
초록· 키워드
오류제보하기
Deep learning has become one of the most powerful prediction approaches, and it can be used to solve classification and regression problems. We present a novel deep learning-based indoor Wi-Fi path loss modeling approach. Specifically, we propose a local area multi-line scanning algorithm that generates input images based on measurement locations and a floor plan. As the input images contain information regarding the propagation environment between the fixed access points (APs) and measurement locations, a convolutional neural network (CNN) model can be trained to learn the features of the indoor environment and approximate the underlying functions of the Wi-Fi signal propagation. The proposed deep learning-based indoor path loss model can achieve superior performance over 3D ray-tracing methods. The average root mean square error (RMSE) between the predicted and measured received signal strength values in the two scenarios is 4.63 dB.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. DEEP LEARNING-BASED PATH LOSS MODELING SYSTEM
IV. EXPERIMENTAL RESULTS
V. CONCLUSION
REFERENCES
참고문헌 (38)
참고문헌 신청
A. Bhuvaneshwari, R. Hemalatha, and T. Satyasavithri, “Path loss prediction analysis by ray tracing approach for NLOS indoor propagation,” in Proceedings of 2015 International Conference on Signal Processing and Communication Engineering Systems, Guntur, India, 2015, pp. 486-491.
H. K. Rath, S. Timmadasari, B. Panigrahi, and A. Simha, “Realistic indoor path loss modeling for regular WiFi operations in India,” in Proceedings of 2017 23rd National Conference on Communications (NCC), Chennai, India, 2017, pp. 1-6.
I. Vilovic and N. Burum, “Neural network prediction of signal strength for irregular indoor environments,” Automatika, vol. 56, no. 1, pp. 55-68, 2015. https://doi.org/10.7305/automatika. 2015.04.463
B. O. H. Akinwole and J. J. Biebuma, “Comparative analysis of empirical path loss model for cellular transmission in rivers state,” American Journal of Engineering Research, vol. 2, no. 8, pp. 24-31, 2013.
S. I. Popoola, E. Adetiba, A. A. Atayero, N. Faruk, and C. T. Calafate, “Optimal model for path loss predictions using feed-forward neural networks,” Cogent Engineering, vol. 5, no. 1, article no. 1444345, 2018. https://doi.org/10.1080/23311916.2018.1444345
H. K. Rath, S. Timmadasari, B. Panigrahi, and A. Simha, “Realistic indoor path loss modeling for regular WiFi operations in India,” in Proceedings of 2017 23rd National Conference on Communications (NCC), Chennai, India, 2017, pp. 1-6.
I. Vilovic and N. Burum, “Neural network prediction of signal strength for irregular indoor environments,” Automatika, vol. 56, no. 1, pp. 55-68, 2015. https://doi.org/10.7305/automatika. 2015.04.463
B. O. H. Akinwole and J. J. Biebuma, “Comparative analysis of empirical path loss model for cellular transmission in rivers state,” American Journal of Engineering Research, vol. 2, no. 8, pp. 24-31, 2013.
S. I. Popoola, E. Adetiba, A. A. Atayero, N. Faruk, and C. T. Calafate, “Optimal model for path loss predictions using feed-forward neural networks,” Cogent Engineering, vol. 5, no. 1, article no. 1444345, 2018. https://doi.org/10.1080/23311916.2018.1444345
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