Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications

Teguh Indra Bayu, Yung-Fa Huang (), Jeang-Kuo Chen, Cheng-Hsiung Hsieh (), Budhi Kristianto, Erwien Christianto and Suharyadi Suharyadi
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Teguh Indra Bayu: Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia
Yung-Fa Huang: Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Jeang-Kuo Chen: Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Cheng-Hsiung Hsieh: Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Budhi Kristianto: Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia
Erwien Christianto: Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia
Suharyadi Suharyadi: Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia

Future Internet, 2025, vol. 17, issue 1, 1-19

Abstract: The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability ( P r k ) 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 P r k 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.

Keywords: cellular vehicle-to-everything; modulation coding scheme; packet reception ratio; fuzzy inference system; Q-learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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