Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications

Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><...

Full description

Saved in:
Bibliographic Details
Main Authors: Teguh Indra Bayu, Yung-Fa Huang, Jeang-Kuo Chen, Cheng-Hsiung Hsieh, Budhi Kristianto, Erwien Christianto, Suharyadi Suharyadi
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Future Internet
Subjects:
cellular vehicle-to-everything
modulation coding scheme
packet reception ratio
fuzzy inference system
Q-learning
Online Access:https://www.mdpi.com/1999-5903/17/1/46
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>r</mi><mi>k</mi></mrow></msub></mrow></semantics></math></inline-formula>) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>r</mi><mi>k</mi></mrow></msub></mrow></semantics></math></inline-formula> and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters.
ISSN:1999-5903

Similar Items