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Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models.

Alan Torres-Alvarado1, Luis Alberto Morales-Rosales2, Ignacio Algredo-Badillo1

  • 1Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla 72840, Mexico.

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Summary
This summary is machine-generated.

This study introduces a hardware-based machine learning approach to assess vehicular network channel quality. Decision Trees offer the best trade-off for efficient, low-power channel selection in Software-defined vehicular networks.

Keywords:
FPGAchannel quality classificationhardware implementationmachine learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Vehicular networks like Software-defined vehicular networks (SDVN) and Vehicular ad-hoc networks (VANETs) require channel quality assessment for adaptive behavior.
  • Transmission channel quality impacts network performance, affecting bandwidth utilization and necessitating fault-tolerant schemes that can reduce speed and increase energy consumption.
  • An efficient, low-power mechanism is needed to sense channel quality and automatically select optimal transmission channels.

Purpose of the Study:

  • To analyze the trade-offs of hardware implementations for identifying high or low quality communication channels.
  • To evaluate the effectiveness of four machine learning algorithms for real-time channel quality sensing and selection.
  • To identify the most efficient and accurate algorithm for hardware deployment in vehicular networks.

Main Methods:

  • Implemented and evaluated four machine learning algorithms: Decision Trees, Multi-Layer Perceptron, Logistic Regression, and Support Vector Machines.
  • Performed a hardware implementation trade-off analysis to measure performance metrics.
  • Focused on accuracy and efficiency (Mbps/LUT) for channel quality classification.

Main Results:

  • The Decision Tree algorithm achieved the highest accuracy at 95.01%.
  • The Decision Tree implementation demonstrated superior efficiency with 9.83 Mbps/LUT.
  • A Decision Tree with a depth of five provided the optimal balance between accuracy and hardware resource utilization.

Conclusions:

  • Hardware-based Decision Trees offer an efficient and accurate solution for real-time vehicular communication channel quality assessment.
  • This approach enables adaptive channel selection, optimizing bandwidth use and potentially reducing the overhead of fault-tolerant schemes.
  • The findings contribute to the development of more robust and energy-efficient vehicular communication systems.