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><...
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| author | Teguh Indra Bayu Yung-Fa Huang Jeang-Kuo Chen Cheng-Hsiung Hsieh Budhi Kristianto Erwien Christianto Suharyadi Suharyadi |
| author_facet | Teguh Indra Bayu Yung-Fa Huang Jeang-Kuo Chen Cheng-Hsiung Hsieh Budhi Kristianto Erwien Christianto Suharyadi Suharyadi |
| author_sort | Teguh Indra Bayu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-537f1c6d83ba412bad902727fd380edc |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-537f1c6d83ba412bad902727fd380edc2025-01-24T13:33:41ZengMDPI AGFuture Internet1999-59032025-01-011714610.3390/fi17010046Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular CommunicationsTeguh Indra Bayu0Yung-Fa Huang1Jeang-Kuo Chen2Cheng-Hsiung Hsieh3Budhi Kristianto4Erwien Christianto5Suharyadi Suharyadi6Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, IndonesiaDepartment of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, TaiwanDepartment of Information Management, Chaoyang University of Technology, Taichung 413310, TaiwanDepartment of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, TaiwanFaculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, IndonesiaFaculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, IndonesiaFaculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, IndonesiaThe 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.https://www.mdpi.com/1999-5903/17/1/46cellular vehicle-to-everythingmodulation coding schemepacket reception ratiofuzzy inference systemQ-learning |
| spellingShingle | Teguh Indra Bayu Yung-Fa Huang Jeang-Kuo Chen Cheng-Hsiung Hsieh Budhi Kristianto Erwien Christianto Suharyadi Suharyadi Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications Future Internet cellular vehicle-to-everything modulation coding scheme packet reception ratio fuzzy inference system Q-learning |
| title | Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications |
| title_full | Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications |
| title_fullStr | Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications |
| title_full_unstemmed | Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications |
| title_short | Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications |
| title_sort | advanced optimization algorithm combining a fuzzy inference system for vehicular communications |
| topic | cellular vehicle-to-everything modulation coding scheme packet reception ratio fuzzy inference system Q-learning |
| url | https://www.mdpi.com/1999-5903/17/1/46 |
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