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arXiv 2102.13554cs.NIeess.SP
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上行链路URLLC业务的自适应传输参数选择算法

Adaptive Transmission Parameters Selection Algorithm for URLLC Traffic in Uplink

Aleksei Shahsin, Andrey Belogaev, Artem Krasilov, Evgeny Khorov

AI总结:

针对5G上行URLLC免授权接入中固定传输参数导致网络容量低的问题,本文提出基于gNB信噪比统计分析的自适应传输参数选择算法,在满足时延和可靠性要求的同时将信道资源消耗降低两倍以上。

摘要:

超可靠低时延通信(URLLC)是5G蜂窝系统的一项新特性。为满足上行数据传输严格的URLLC要求,5G系统规范引入了免授权信道接入方法。根据该方法,用户设备(UE)无需向基站(gNB)请求信道资源即可执行分组传输。采用免授权信道接入时,gNB在长期时间尺度上配置上行传输参数。由于信道质量在时域和频域上可能发生显著变化,gNB应选择稳健的传输参数以满足URLLC要求。许多现有研究考虑固定的稳健上行传输参数选择,即使对于信道条件较差的UE也能满足要求。然而,选择的传输参数越稳健,网络容量就越低。本文提出一种自适应算法,该算法基于gNB处的信噪比统计分析,根据信道质量选择传输参数。使用NS-3获得的仿真结果表明,与固定传输参数选择相比,该算法能够在满足URLLC时延和可靠性要求的同时,将信道资源消耗降低两倍以上。

英文摘要:

Ultra-Reliable Low-Latency Communications (URLLC) is a novel feature of 5G cellular systems. To satisfy strict URLLC requirements for uplink data transmission, the specifications of 5G systems introduce the grant-free channel access method. According to this method, a User Equipment (UE) performs packet transmission without requesting channel resources from a base station (gNB). With the grant-free channel access, the gNB configures the uplink transmission parameters in a long-term time scale. Since the channel quality can significantly change in time and frequency domains, the gNB should select robust transmission parameters to satisfy the URLLC requirements. Many existing studies consider fixed robust uplink transmission parameter selection that allows satisfying the requirements even for UEs with poor channel conditions. However, the more robust transmission parameters are selected, the lower is the network capacity. In this paper, we propose an adaptive algorithm that selects the transmission parameters depending on the channel quality based on the signal-to-noise ratio statistics analysis at the gNB. Simulation results obtained with NS-3 show that the algorithm allows meeting the URLLC latency and reliability requirements while reducing the channel resource consumption more than twice in comparison with the fixed transmission parameters selection.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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