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.

翻译：近期，电动汽车（EV）中的新型干扰源（如转换器开关和/或电池充电器）被发现会降低无线数字传输系统的性能。测量结果表明，此类干扰具有脉冲特性且随时间剧烈变化。本文利用电动汽车测试平台采集的实测数据，对数字音频广播频段的脉冲干扰进行了分析。进一步地，我们基于分析结果建立了相应的干扰模型。具体而言，我们研究了干扰的时间特性，并确认其幅值确实呈现脉冲行为。研究结果显示，脉冲事件跨越连续的接收信号样本，表明其具有突发性。为此，我们基于数据驱动方式对经典的突发脉冲干扰模型——马尔可夫-米德尔顿模型——进行了改进，以生成合成噪声实现。我们基于所提模型研究了最优符号检测器设计，并证明相较于基于加性高斯白噪声假设的传统检测器，所提方法具有显著的性能增益。