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Article Dans Une Revue Sensors Année : 2021

V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier

Résumé

Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time-frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.
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Dates et versions

hal-03275555 , version 1 (01-07-2021)

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Camelia Skiribou, Fouzia Elbahhar. V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier. Sensors, 2021, 21 (13), pp1-14. ⟨10.3390/s21134286⟩. ⟨hal-03275555⟩
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