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Electrical Engineering and Systems Science > Signal Processing

arXiv:1903.01576 (eess)
[Submitted on 4 Mar 2019 (v1), last revised 6 Mar 2019 (this version, v2)]

Title:V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures

Authors:Hossein Nourkhiz Mahjoub, Behrad Toghi, S M Osman Gani, Yaser P. Fallah
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Abstract:Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. The model-based communication (MBC) is a recently-explored scalability solution proposed in the literature, which has shown a promising potential to reduce the channel congestion to a great extent. In this work, based on the MBC notion, a technology-agnostic hybrid model selection policy for Vehicle-to-Everything (V2X) communication is proposed which benefits from the characteristics of the non-parametric Bayesian inference techniques, specifically Gaussian Processes. The results show the effectiveness of the proposed communication architecture on both reducing the required message exchange rate and increasing the remote agent tracking precision.
Comments: Accepted for Oral Presentation at the 13th IEEE Systems Conference (SysCon 2019)
Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Cite as: arXiv:1903.01576 [eess.SP]
  (or arXiv:1903.01576v2 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.1903.01576

Submission history

From: Hossein Nourkhiz Mahjoub [view email]
[v1] Mon, 4 Mar 2019 22:53:08 UTC (4,266 KB)
[v2] Wed, 6 Mar 2019 02:08:14 UTC (2,709 KB)
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