Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach
Conventional optimization-driven secure relay selection relies on maximization algorithm and accurate channel state information (CSI) of both legitimate and eavesdropper channels. Particularly, estimating and collecting accurate eavesdropper CSI is a difficult task. In this paper, we exploit the ben...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8957152/ |
| _version_ | 1818433472084049920 |
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| author | Xiaowei Wang Feng Liu |
| author_facet | Xiaowei Wang Feng Liu |
| author_sort | Xiaowei Wang |
| collection | DOAJ |
| description | Conventional optimization-driven secure relay selection relies on maximization algorithm and accurate channel state information (CSI) of both legitimate and eavesdropper channels. Particularly, estimating and collecting accurate eavesdropper CSI is a difficult task. In this paper, we exploit the benefits of machine learning in solving secure relay selection problem from a data-driven perspective. We convert secure relay selection to a multiclass-classification problem and solve it by a decision-tree-based scheme, which is composed of three phases - preparing training data, building decision tree and predicting relay selection. To meet decision tree's requirement that input features must take discrete values, a feature extraction method is proposed to generate discrete input by quantizing the accurate CSI of legitimate and eavesdropper channels. By this means, the decision-tree-based relay selection only requires quantized CSI feedback which takes substantially fewer bits in predicting phase. For the purpose of optimizing quantization parameters and enhancing decision tree prediction, we further derive three splitting criteria, i.e. information gain, information gain ratio and Gini index. Simulation results show that if the quantization parameters are set properly, the proposed decision-tree-based scheme can achieve satisfactory performance in terms of average secrecy rate while reducing computational complexity and feedback amount. |
| first_indexed | 2024-12-14T16:21:38Z |
| format | Article |
| id | doaj.art-f74c87cc8ce44cd5b71831abb0a24e32 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:21:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f74c87cc8ce44cd5b71831abb0a24e322022-12-21T22:54:47ZengIEEEIEEE Access2169-35362020-01-018121051211610.1109/ACCESS.2020.29659638957152Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree ApproachXiaowei Wang0https://orcid.org/0000-0003-2850-3585Feng Liu1https://orcid.org/0000-0002-7912-2275College of Information Engineering, Shanghai Maritime University, Shanghai, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai, ChinaConventional optimization-driven secure relay selection relies on maximization algorithm and accurate channel state information (CSI) of both legitimate and eavesdropper channels. Particularly, estimating and collecting accurate eavesdropper CSI is a difficult task. In this paper, we exploit the benefits of machine learning in solving secure relay selection problem from a data-driven perspective. We convert secure relay selection to a multiclass-classification problem and solve it by a decision-tree-based scheme, which is composed of three phases - preparing training data, building decision tree and predicting relay selection. To meet decision tree's requirement that input features must take discrete values, a feature extraction method is proposed to generate discrete input by quantizing the accurate CSI of legitimate and eavesdropper channels. By this means, the decision-tree-based relay selection only requires quantized CSI feedback which takes substantially fewer bits in predicting phase. For the purpose of optimizing quantization parameters and enhancing decision tree prediction, we further derive three splitting criteria, i.e. information gain, information gain ratio and Gini index. Simulation results show that if the quantization parameters are set properly, the proposed decision-tree-based scheme can achieve satisfactory performance in terms of average secrecy rate while reducing computational complexity and feedback amount.https://ieeexplore.ieee.org/document/8957152/Physical-layer securityrelay selectionmachine learningdecision treesplitting criterion |
| spellingShingle | Xiaowei Wang Feng Liu Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach IEEE Access Physical-layer security relay selection machine learning decision tree splitting criterion |
| title | Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach |
| title_full | Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach |
| title_fullStr | Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach |
| title_full_unstemmed | Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach |
| title_short | Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach |
| title_sort | data driven relay selection for physical layer security a decision tree approach |
| topic | Physical-layer security relay selection machine learning decision tree splitting criterion |
| url | https://ieeexplore.ieee.org/document/8957152/ |
| work_keys_str_mv | AT xiaoweiwang datadrivenrelayselectionforphysicallayersecurityadecisiontreeapproach AT fengliu datadrivenrelayselectionforphysicallayersecurityadecisiontreeapproach |