Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario
The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of <inline-formula> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula> generation (5G) mobile communication system networks. There is...
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2019-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/8718287/ |
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| author | Jingxuan Zhang Xiaodong Xu Kangjie Zhang Bufang Zhang Xiaofeng Tao Ping Zhang |
| author_facet | Jingxuan Zhang Xiaodong Xu Kangjie Zhang Bufang Zhang Xiaofeng Tao Ping Zhang |
| author_sort | Jingxuan Zhang |
| collection | DOAJ |
| description | The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of <inline-formula> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula> generation (5G) mobile communication system networks. There is an obvious difference in service requirements between different scenarios. When multi-scenario services coexist in the 5G networks, exploring optimized resource scheduling and allocation strategies become a critical issue. The 5G New Radio (NR) and numerology technologies have been standardized, which lay the foundation for flexible frame structure and adaptive scheduling. In this paper, we propose the self-adaptive flexible transmission time interval (TTI) scheduling (SAFE-TS) strategy in the eMBB and uRLLC coexistence scenario. Machine learning (ML) is applied to achieve flexible TTI scheduling. Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. Compared with the existing ML methods, the proposed algorithm has a performance improvement in selecting TTI, especially for the uRLLC services. Then, the TTI selection results will be the basis of system resource scheduling and allocation. The simulation results prove that the proposed SAFE-TS effectively reduce the delay and packet loss rate of the uRLLC services while guaranteeing the eMBB requirements. Therefore, it is highly recommended that flexible TTI scheduling should be applied in the construction of the 5G networks to achieve superior network performances. |
| format | Article |
| id | doaj-art-d19dc4493ae6437ba66164e50684f600 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d19dc4493ae6437ba66164e50684f6002024-12-18T00:00:48ZengIEEEIEEE Access2169-35362019-01-017658116582010.1109/ACCESS.2019.29177518718287Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence ScenarioJingxuan Zhang0https://orcid.org/0000-0003-0236-5735Xiaodong Xu1https://orcid.org/0000-0003-4245-5989Kangjie Zhang2https://orcid.org/0000-0002-1801-5476Bufang Zhang3Xiaofeng Tao4https://orcid.org/0000-0001-9518-1622Ping Zhang5National Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, ChinaNational Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, ChinaNational Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, ChinaNational Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, ChinaNational Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, ChinaNational Engineering Laboratory for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, ChinaThe enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of <inline-formula> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula> generation (5G) mobile communication system networks. There is an obvious difference in service requirements between different scenarios. When multi-scenario services coexist in the 5G networks, exploring optimized resource scheduling and allocation strategies become a critical issue. The 5G New Radio (NR) and numerology technologies have been standardized, which lay the foundation for flexible frame structure and adaptive scheduling. In this paper, we propose the self-adaptive flexible transmission time interval (TTI) scheduling (SAFE-TS) strategy in the eMBB and uRLLC coexistence scenario. Machine learning (ML) is applied to achieve flexible TTI scheduling. Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. Compared with the existing ML methods, the proposed algorithm has a performance improvement in selecting TTI, especially for the uRLLC services. Then, the TTI selection results will be the basis of system resource scheduling and allocation. The simulation results prove that the proposed SAFE-TS effectively reduce the delay and packet loss rate of the uRLLC services while guaranteeing the eMBB requirements. Therefore, it is highly recommended that flexible TTI scheduling should be applied in the construction of the 5G networks to achieve superior network performances.https://ieeexplore.ieee.org/document/8718287/Flexible TTI schedulingmachine learningdelaycontrol overheadeMBBuRLLC |
| spellingShingle | Jingxuan Zhang Xiaodong Xu Kangjie Zhang Bufang Zhang Xiaofeng Tao Ping Zhang Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario IEEE Access Flexible TTI scheduling machine learning delay control overhead eMBB uRLLC |
| title | Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario |
| title_full | Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario |
| title_fullStr | Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario |
| title_full_unstemmed | Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario |
| title_short | Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario |
| title_sort | machine learning based flexible transmission time interval scheduling for embb and urllc coexistence scenario |
| topic | Flexible TTI scheduling machine learning delay control overhead eMBB uRLLC |
| url | https://ieeexplore.ieee.org/document/8718287/ |
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