Repetition with Learning Approaches in Massive Machine Type Communications
In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/22/3649 |
| _version_ | 1797465530376912896 |
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| author | Li-Sheng Chen Chih-Hsiang Ho Cheng-Chang Chen Yu-Shan Liang Sy-Yen Kuo |
| author_facet | Li-Sheng Chen Chih-Hsiang Ho Cheng-Chang Chen Yu-Shan Liang Sy-Yen Kuo |
| author_sort | Li-Sheng Chen |
| collection | DOAJ |
| description | In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy consumption. An insufficient number of repetitions prevents the successful deciphering of the data by the receivers, leading to a high bit error rate. The present study developed adaptive repetition approaches with the k-nearest neighbor (KNN) and support vector machine (SVM) to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in the 5G mMTC scenario. The simulation results showed that the proposed repetition with the learning approach effectively improved the probability of successful transmission, the resource utilization, the average number of repetitions, and the average energy consumption. It is therefore more suitable for the eMTC system in the mMTC scenario than the common lookup table. |
| first_indexed | 2024-03-09T18:23:44Z |
| format | Article |
| id | doaj.art-b9e0a664952a45d2910c895759eedcf0 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T18:23:44Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b9e0a664952a45d2910c895759eedcf02023-11-24T08:08:13ZengMDPI AGElectronics2079-92922022-11-011122364910.3390/electronics11223649Repetition with Learning Approaches in Massive Machine Type CommunicationsLi-Sheng Chen0Chih-Hsiang Ho1Cheng-Chang Chen2Yu-Shan Liang3Sy-Yen Kuo4Department of Communications Engineering, Feng Chia University, Taichung 407, TaiwanInstitute for Information Industry, Taipei 106, TaiwanBureau of Standards, Metrology and Inspection, M. O. E. A., Taipei 100, TaiwanDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 824, TaiwanDepartment of Electrical Engineering, National Taiwan University, Taipei 100, TaiwanIn the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy consumption. An insufficient number of repetitions prevents the successful deciphering of the data by the receivers, leading to a high bit error rate. The present study developed adaptive repetition approaches with the k-nearest neighbor (KNN) and support vector machine (SVM) to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in the 5G mMTC scenario. The simulation results showed that the proposed repetition with the learning approach effectively improved the probability of successful transmission, the resource utilization, the average number of repetitions, and the average energy consumption. It is therefore more suitable for the eMTC system in the mMTC scenario than the common lookup table.https://www.mdpi.com/2079-9292/11/22/3649massive machine type communications (mMTC)enhanced machine type communication (eMTC)repetitionmachine learningk-nearest neighbor (KNN)support vector machine (SVM) |
| spellingShingle | Li-Sheng Chen Chih-Hsiang Ho Cheng-Chang Chen Yu-Shan Liang Sy-Yen Kuo Repetition with Learning Approaches in Massive Machine Type Communications Electronics massive machine type communications (mMTC) enhanced machine type communication (eMTC) repetition machine learning k-nearest neighbor (KNN) support vector machine (SVM) |
| title | Repetition with Learning Approaches in Massive Machine Type Communications |
| title_full | Repetition with Learning Approaches in Massive Machine Type Communications |
| title_fullStr | Repetition with Learning Approaches in Massive Machine Type Communications |
| title_full_unstemmed | Repetition with Learning Approaches in Massive Machine Type Communications |
| title_short | Repetition with Learning Approaches in Massive Machine Type Communications |
| title_sort | repetition with learning approaches in massive machine type communications |
| topic | massive machine type communications (mMTC) enhanced machine type communication (eMTC) repetition machine learning k-nearest neighbor (KNN) support vector machine (SVM) |
| url | https://www.mdpi.com/2079-9292/11/22/3649 |
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