Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments
Wireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning me...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8888263/ |
| _version_ | 1818665389875265536 |
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| author | Jinxiao Wen Yan Zhang Guanshu Yang Zunwen He Wancheng Zhang |
| author_facet | Jinxiao Wen Yan Zhang Guanshu Yang Zunwen He Wancheng Zhang |
| author_sort | Jinxiao Wen |
| collection | DOAJ |
| description | Wireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning methods, including back propagation neural network (BPNN), support vector regression (SVR), random forest, and AdaBoost, are used to build path loss prediction models for an MD-82 aircraft cabin. Firstly, machine-learning-based models are designed to predict the path loss values at different locations at a fixed frequency. It is shown that these models fit the measured data well, e.g., at 2.4 GHz central frequency the root mean square errors (RMSEs) of BPNN, SVR, random forest, and AdaBoost predictors are 1.90 dB, 2.20 dB, 1.76 dB, and 2.12 dB. Subsequent research is engaged to forecast path loss at a new frequency based on available information at known frequencies. Additionally, to solve the data limitation problem at the new frequency, we propose a path loss prediction scheme combining empirical models and machine-learning-based models. This scheme uses estimated values generated by the empirical model according to prior information to expand the training set. To verify the performance of this scheme, measured samples at 2.4 GHz and 3.52 GHz, as well as samples generated by the empirical model are employed as the training set for the path loss prediction at 5.8 GHz. The RMSEs of BPNN, SVR, random forest, and AdaBoost models are 2.49 dB, 2.78 dB, 2.54 dB, and 3.76 dB. In contrast, without samples generated by the empirical model, the RMSEs of those models are 3.84 dB, 4.94 dB, 6.57 dB, and 6.77 dB. Results show that the proposed data expansion scheme improves prediction performance when there are few measurement samples at the new frequency. |
| first_indexed | 2024-12-17T05:47:52Z |
| format | Article |
| id | doaj.art-f5891d9193c9487e9a7a7c95c85c8892 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:47:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f5891d9193c9487e9a7a7c95c85c88922022-12-21T22:01:16ZengIEEEIEEE Access2169-35362019-01-01715925115926110.1109/ACCESS.2019.29506348888263Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin EnvironmentsJinxiao Wen0https://orcid.org/0000-0002-9639-7473Yan Zhang1https://orcid.org/0000-0002-2168-9674Guanshu Yang2https://orcid.org/0000-0001-9043-2090Zunwen He3https://orcid.org/0000-0002-5584-4855Wancheng Zhang4https://orcid.org/0000-0002-2039-6431School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaWireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning methods, including back propagation neural network (BPNN), support vector regression (SVR), random forest, and AdaBoost, are used to build path loss prediction models for an MD-82 aircraft cabin. Firstly, machine-learning-based models are designed to predict the path loss values at different locations at a fixed frequency. It is shown that these models fit the measured data well, e.g., at 2.4 GHz central frequency the root mean square errors (RMSEs) of BPNN, SVR, random forest, and AdaBoost predictors are 1.90 dB, 2.20 dB, 1.76 dB, and 2.12 dB. Subsequent research is engaged to forecast path loss at a new frequency based on available information at known frequencies. Additionally, to solve the data limitation problem at the new frequency, we propose a path loss prediction scheme combining empirical models and machine-learning-based models. This scheme uses estimated values generated by the empirical model according to prior information to expand the training set. To verify the performance of this scheme, measured samples at 2.4 GHz and 3.52 GHz, as well as samples generated by the empirical model are employed as the training set for the path loss prediction at 5.8 GHz. The RMSEs of BPNN, SVR, random forest, and AdaBoost models are 2.49 dB, 2.78 dB, 2.54 dB, and 3.76 dB. In contrast, without samples generated by the empirical model, the RMSEs of those models are 3.84 dB, 4.94 dB, 6.57 dB, and 6.77 dB. Results show that the proposed data expansion scheme improves prediction performance when there are few measurement samples at the new frequency.https://ieeexplore.ieee.org/document/8888263/Aircraft cabindata expansionmachine learningpath loss predictionpropagation characteristics |
| spellingShingle | Jinxiao Wen Yan Zhang Guanshu Yang Zunwen He Wancheng Zhang Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments IEEE Access Aircraft cabin data expansion machine learning path loss prediction propagation characteristics |
| title | Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments |
| title_full | Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments |
| title_fullStr | Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments |
| title_full_unstemmed | Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments |
| title_short | Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments |
| title_sort | path loss prediction based on machine learning methods for aircraft cabin environments |
| topic | Aircraft cabin data expansion machine learning path loss prediction propagation characteristics |
| url | https://ieeexplore.ieee.org/document/8888263/ |
| work_keys_str_mv | AT jinxiaowen pathlosspredictionbasedonmachinelearningmethodsforaircraftcabinenvironments AT yanzhang pathlosspredictionbasedonmachinelearningmethodsforaircraftcabinenvironments AT guanshuyang pathlosspredictionbasedonmachinelearningmethodsforaircraftcabinenvironments AT zunwenhe pathlosspredictionbasedonmachinelearningmethodsforaircraftcabinenvironments AT wanchengzhang pathlosspredictionbasedonmachinelearningmethodsforaircraftcabinenvironments |