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

Alan Torres-Alvarado; Luis Alberto Morales-Rosales; Ignacio Algredo-Badillo; Francisco López-Huerta; Mariana Lobato-Baez; López-Pimentel, Juan Carlos

doi:10.3390/s22072497

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

Journal

Sensors

ISSN

1424-8220

Date Issued

2022

Author(s)

Alan Torres-Alvarado

Luis Alberto Morales-Rosales

Ignacio Algredo-Badillo

Francisco López-Huerta

Mariana Lobato-Baez

López-Pimentel, Juan Carlos

Facultad de Ingeniería - CampGDL

Type

text::journal::journal article

DOI

10.3390/s22072497

URL

https://scripta.up.edu.mx/handle/20.500.12552/4042

Abstract

The latest generation of communication networks, such as SDVN (Software-defined vehicular network) and VANETs (Vehicular ad-hoc networks), should evaluate their communication channels to adapt their behavior. The quality of the communication in data networks depends on the behavior of the transmission channel selected to send the information. Transmission channels can be affected by diverse problems ranging from physical phenomena (e.g., weather, cosmic rays) to interference or faults inherent to data spectra. In particular, if the channel has a good transmission quality, we might maximize the bandwidth use. Otherwise, although fault-tolerant schemes degrade the transmission speed by solving errors or failures should be included, these schemes spend more energy and are slower due to requesting lost packets (recovery). In this sense, one of the open problems in communications is how to design and implement an efficient and low-power-consumption mechanism capable of sensing the quality of the channel and automatically making the adjustments to select the channel over which transmit. In this work, we present a trade-off analysis based on hardware implementation to identify if a channel has a low or high quality, implementing four machine learning algorithms: Decision Trees, Multi-Layer Perceptron, Logistic Regression, and Support Vector Machines. We obtained the best trade-off with an accuracy of 95.01% and efficiency of 9.83 Mbps/LUT (LookUp Table) with a hardware implementation of a Decision Tree algorithm with a depth of five.