Analysis of Impulsive Interference in Digital Audio Broadcasting Systems in Electric Vehicles
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909206211198976 |
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| author | Chen, Chin-Hung Huang, Wen-Hung Karanov, Boris Young, Alex Wu, Yan van Houtum, Wim |
| author_facet | Chen, Chin-Hung Huang, Wen-Hung Karanov, Boris Young, Alex Wu, Yan van Houtum, Wim |
| contents | Recently, new types of interference in electric vehicles (EVs), such as converters switching and/or battery chargers, have been found to degrade the performance of wireless digital transmission systems. Measurements show that such an interference is characterized by impulsive behavior and is widely varying in time. This paper uses recorded data from our EV testbed to analyze the impulsive interference in the digital audio broadcasting band. Moreover, we use our analysis to obtain a corresponding interference model. In particular, we studied the temporal characteristics of the interference and confirmed that its amplitude indeed exhibits an impulsive behavior. Our results show that impulsive events span successive received signal samples and thus indicate a bursty nature. To this end, we performed a data-driven modification of a well-established model for bursty impulsive interference, the Markov-Middleton model, to produce synthetic noise realization. We investigate the optimal symbol detector design based on the proposed model and show significant performance gains compared to the conventional detector based on the additive white Gaussian noise assumption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis of Impulsive Interference in Digital Audio Broadcasting Systems in Electric Vehicles Chen, Chin-Hung Huang, Wen-Hung Karanov, Boris Young, Alex Wu, Yan van Houtum, Wim Signal Processing Machine Learning Recently, new types of interference in electric vehicles (EVs), such as converters switching and/or battery chargers, have been found to degrade the performance of wireless digital transmission systems. Measurements show that such an interference is characterized by impulsive behavior and is widely varying in time. This paper uses recorded data from our EV testbed to analyze the impulsive interference in the digital audio broadcasting band. Moreover, we use our analysis to obtain a corresponding interference model. In particular, we studied the temporal characteristics of the interference and confirmed that its amplitude indeed exhibits an impulsive behavior. Our results show that impulsive events span successive received signal samples and thus indicate a bursty nature. To this end, we performed a data-driven modification of a well-established model for bursty impulsive interference, the Markov-Middleton model, to produce synthetic noise realization. We investigate the optimal symbol detector design based on the proposed model and show significant performance gains compared to the conventional detector based on the additive white Gaussian noise assumption. |
| title | Analysis of Impulsive Interference in Digital Audio Broadcasting Systems in Electric Vehicles |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2405.10828 |