{"title":"5G异构网络中的D2D组播","authors":"Rafael Kaliski","doi":"10.1109/Indo-TaiwanICAN48429.2020.9181348","DOIUrl":null,"url":null,"abstract":"The goal of 5G wireless networks is to support a diverse set of requirements spanning low-latency, high connectivity, and high bit-rates. Support for the multitude of deployment scenarios is enabled by 5G Quality of Service (QoS) flows. In 5G, an inbound service from outside the cellular network can be separated into multiple QoS flows and scheduled onto multiple data bearers; each 5G data bearer meets specific QoS requirements. These requirements include extended networks, as enabled by UE-to-Network Relays. Typical examples are the networks formed by Device to Device (D2D) User Equipment (UEs) and Internet of Things (IoT) devices. Traditional latency-driven assignment methodologies typically assign a fixed Latency / Sub-Carrier Spacing (SCS) to each multicast flow, as directed by the QoS requirements. A drawback to traditional approaches is they do not account for the impact of multi-path / delay spread, as such they are unable to efficiently utilize the allocated radio spectrum in high delay spread radio environments; low latency flows are more sensitive to high delay spread. In this research work we derive an optimal QoS to 5G data bearer mapping for an extended network, subject to the constraints imposed by UEs and the UE-to-Network Relay. Compared to legacy multicast methodologies, our methodology can achieve an equivalent or higher throughput while minimizing deviation from the original latency requirements.","PeriodicalId":171125,"journal":{"name":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"D2D Groupcast in 5G Heterogeneous Networks\",\"authors\":\"Rafael Kaliski\",\"doi\":\"10.1109/Indo-TaiwanICAN48429.2020.9181348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of 5G wireless networks is to support a diverse set of requirements spanning low-latency, high connectivity, and high bit-rates. Support for the multitude of deployment scenarios is enabled by 5G Quality of Service (QoS) flows. In 5G, an inbound service from outside the cellular network can be separated into multiple QoS flows and scheduled onto multiple data bearers; each 5G data bearer meets specific QoS requirements. These requirements include extended networks, as enabled by UE-to-Network Relays. Typical examples are the networks formed by Device to Device (D2D) User Equipment (UEs) and Internet of Things (IoT) devices. Traditional latency-driven assignment methodologies typically assign a fixed Latency / Sub-Carrier Spacing (SCS) to each multicast flow, as directed by the QoS requirements. A drawback to traditional approaches is they do not account for the impact of multi-path / delay spread, as such they are unable to efficiently utilize the allocated radio spectrum in high delay spread radio environments; low latency flows are more sensitive to high delay spread. In this research work we derive an optimal QoS to 5G data bearer mapping for an extended network, subject to the constraints imposed by UEs and the UE-to-Network Relay. Compared to legacy multicast methodologies, our methodology can achieve an equivalent or higher throughput while minimizing deviation from the original latency requirements.\",\"PeriodicalId\":171125,\"journal\":{\"name\":\"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181348\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The goal of 5G wireless networks is to support a diverse set of requirements spanning low-latency, high connectivity, and high bit-rates. Support for the multitude of deployment scenarios is enabled by 5G Quality of Service (QoS) flows. In 5G, an inbound service from outside the cellular network can be separated into multiple QoS flows and scheduled onto multiple data bearers; each 5G data bearer meets specific QoS requirements. These requirements include extended networks, as enabled by UE-to-Network Relays. Typical examples are the networks formed by Device to Device (D2D) User Equipment (UEs) and Internet of Things (IoT) devices. Traditional latency-driven assignment methodologies typically assign a fixed Latency / Sub-Carrier Spacing (SCS) to each multicast flow, as directed by the QoS requirements. A drawback to traditional approaches is they do not account for the impact of multi-path / delay spread, as such they are unable to efficiently utilize the allocated radio spectrum in high delay spread radio environments; low latency flows are more sensitive to high delay spread. In this research work we derive an optimal QoS to 5G data bearer mapping for an extended network, subject to the constraints imposed by UEs and the UE-to-Network Relay. Compared to legacy multicast methodologies, our methodology can achieve an equivalent or higher throughput while minimizing deviation from the original latency requirements.