Analysis of Impulsive Interference in Digital Audio Broadcasting Systems in Electric Vehicles
- URL: http://arxiv.org/abs/2405.10828v1
- Date: Fri, 17 May 2024 14:48:37 GMT
- Title: Analysis of Impulsive Interference in Digital Audio Broadcasting Systems in Electric Vehicles
- Authors: Chin-Hung Chen, Wen-Hung Huang, Boris Karanov, Alex Young, Yan Wu, Wim van Houtum,
- Abstract summary: impulsive interference in electric vehicles (EVs) has been found to degrade wireless digital transmission systems.
This paper uses recorded data from our EV testbed to analyze the impulsive interference in the digital audio broadcasting band.
Our results show that impulsive events span successive received signal samples and thus indicate a bursty nature.
- Score: 2.056630504912993
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Quantifying Noise of Dynamic Vision Sensor [49.665407116447454]
Dynamic visual sensors (DVS) are characterised by a large amount of background activity (BA) noise.
It is difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques.
A new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA)
arXiv Detail & Related papers (2024-04-02T13:43:08Z) - Modelling non-Markovian noise in driven superconducting qubits [2.7648976108201815]
Non-Markovian noise can be a significant source of errors in superconducting qubits.
We develop gate sequences that allow us to characterise and model the effects of non-Markovian noise on both idle and driven qubits.
arXiv Detail & Related papers (2023-06-22T16:30:29Z) - Differentiable Grey-box Modelling of Phaser Effects using Frame-based
Spectral Processing [21.053861381437827]
This work presents a differentiable digital signal processing approach to modelling phaser effects.
The proposed model processes audio in short frames to implement a time-varying filter in the frequency domain.
We show that the model can be trained to emulate an analog reference device, while retaining interpretable and adjustable parameters.
arXiv Detail & Related papers (2023-06-02T07:53:41Z) - Novel features for the detection of bearing faults in railway vehicles [88.89591720652352]
We introduce Mel-Frequency Cepstral Coefficients (MFCCs) and features extracted from the Amplitude Modulation Spectrogram (AMS) as features for the detection of bearing faults.
arXiv Detail & Related papers (2023-04-14T10:09:50Z) - Doubly Stochastic Models: Learning with Unbiased Label Noises and
Inference Stability [85.1044381834036]
We investigate the implicit regularization effects of label noises under mini-batch sampling settings of gradient descent.
We find such implicit regularizer would favor some convergence points that could stabilize model outputs against perturbation of parameters.
Our work doesn't assume SGD as an Ornstein-Uhlenbeck like process and achieve a more general result with convergence of approximation proved.
arXiv Detail & Related papers (2023-04-01T14:09:07Z) - The role of noise in denoising models for anomaly detection in medical
images [62.0532151156057]
Pathological brain lesions exhibit diverse appearance in brain images.
Unsupervised anomaly detection approaches have been proposed using only normal data for training.
We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
arXiv Detail & Related papers (2023-01-19T21:39:38Z) - Macroscopic noise amplification by asymmetric dyads in non-Hermitian
optical systems for generative diffusion models [55.2480439325792]
asymmetric non-Hermitian dyads are promising candidates for efficient sensors and ultra-fast random number generators.
integrated light emission from such asymmetric dyads can be efficiently used for all-optical degenerative diffusion models of machine learning.
arXiv Detail & Related papers (2022-06-24T10:19:36Z) - High-Order Qubit Dephasing at Sweet Spots by Non-Gaussian Fluctuators:
Symmetry Breaking and Floquet Protection [55.41644538483948]
We study the qubit dephasing caused by the non-Gaussian fluctuators.
We predict a symmetry-breaking effect that is unique to the non-Gaussian noise.
arXiv Detail & Related papers (2022-06-06T18:02:38Z) - Deep Interference Mitigation and Denoising of Real-World FMCW Radar
Signals [16.748215232763517]
We evaluate a Convolutional Neural Network (CNN)-based approach for interference mitigation on real-world radar measurements.
We combine real measurements with simulated interference in order to create input-output data suitable for training the model.
arXiv Detail & Related papers (2020-12-04T11:22:13Z) - Fault Detection for Covered Conductors With High-Frequency Voltage
Signals: From Local Patterns to Global Features [5.453001435164266]
We develop an innovative pulse shape characterization method based on clustering techniques.
We construct insightful features and develop a novel machine learning model with a superior detection performance.
The presented model outperforms the winning model in a Kaggle competition and provides the state-of-the-art solution to detect real-time disturbances in the field.
arXiv Detail & Related papers (2020-11-01T02:58:19Z) - Dissipative Rabi model in the dispersive regime [0.0]
We present results on the dispersive regime of the dissipative Rabi model without taking the rotating wave approximation of the underlying Hamiltonian.
Results additionally predict new types of drive induced qubit dissipation and dephasing, not present in previous theories.
arXiv Detail & Related papers (2020-04-06T09:45:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.