| 摘要(英) |
With the launch of 5th generation mobile networks (5G) commercial networks, more and more IoT applications have been proposed. There are some of remote control applications such as factory automation, autonomous driving, and telemedicine surgery which requires real-time and high-precision operations. Based on those applications, it has strict requirements on the success rate and delay of the packet. The 5G white paper proposes three scenarios: Enhanced Mobile Broadband (eMBB), Ultra-reliable and Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), where URLLC is designed for IoT scenarios that require real-time and high-precision. In this scenario, 32bytes packet transmission needs to be completed within 1ms, and the success rate needs to reach 1-10-5. In the 5G standard, Grant-Free and hybrid automatic repeat request (Hybrid Automatic Repeat request, HARQ) are proposed to meet the requirements of URLLC. To reduce the delay, Grant-Free upload data without constructing UL-grant. HARQ is to increase the success rate by retransmitting the same packet multiple times.
This paper proposed two methods: machine learning and mathematical models to achieve the target. The goal of this paper is to make a dynamic adjustment to the maximum amount of re-transmission of HARQ. Also to reduce the burden of equipment caused by continuous retransmissions. Simulation result show that reduce the number of retransmission can improve the performance of eMBB. In adjustment of HARQ repk, the mathematical method is more stable than machine learning, but there are stricter conditions for use. |
| 參考文獻 |
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