Analysis of Impulsive Interference in Digital Audio Broadcasting Systems in Electric Vehicles

TL;DR

Electric vehicles introduce impulsive, bursty interference in the DAB band. The authors combine field measurements with a data-driven adaptation of the Markov-Middleton model, using adaptive thresholds, burst detection, and K-means clustering to estimate state parameters. A MAP detector that accounts for the modified multi-state noise model yields substantial BER improvements over the AWGN baseline, bridging real EV interference with a tractable statistical framework. This work provides a practical pathway to robust DAB reception in EV environments by tying measurements to a parameterized interference model.

Abstract

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.

Figures (6)

• Figure 1: (a) Real part, (b) Imaginary part, (c) magnitude, and (d) roomed in magnitude of the noise and interference measurements from our EV testbed. The green rectangular in (c) represents the captured bursty events with impulsive decision threshold marked with dashed red line
• Figure 2: State diagram representation of a 4-state Markov-Middleton model
• Figure 3: Histograms of (a) I component and (b) Q component of the background noise from the measurements
• Figure 4: Histogram of the magnitude of the identified bursty events from the measurements. Three clusters based on the K-mean algorithm with blue, orange, and yellow bars, attributed to State-1, State-2, and State-3, respectively
• Figure 5: Time domain synthetic bursty impulsive noise samples generated from the modified Markov-Middleton model with parameters in Table \ref{['tab:exp']}