Research on Prediction Based Emergency Resource Allocation in 5G Uplink
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摘要: 作为5G uRLLC的典型应用场景，工业应用对于数据传输的延迟和可靠性要求越来越严苛，且多样化业务带来的多样性数据的融合传输是当前亟待解决的问题，其中高效的无线资源调度以保障各种数据共存互不干扰、稳定可靠地传输和系统安全稳定的运行是重要的挑战之一。为解决无线网络中周期性的监测数据与紧急数据的协同传输问题，该文针对工业多样化业务数据传输场景中的5G上行链路传输，提出一种基于预测的资源分配方案，该方案利用自回归滑动平均(ARMA)模型根据紧急数据的历史传输周期的激活率预测下一传输周期的紧急数据激活率，根据预测激活率动态地为周期数据和紧急数据预留资源，以在满足紧急数据传输条件的前提下最小化对周期数据传输的影响。仿真实验表明，与传统的资源分配方案相比所提方案能有效降低紧急数据传输对周期数据的影响，并能提升频谱资源的利用率。Abstract: As a typical application scenario of 5G uRLLC, the data transmission delay and reliability requirements of industrial applications are more and more stringent, and the convergent transmission of diversified data becomes an urgent problem to be solved. One of the important challenges is the efficient scheduling of wireless resources to ensure the coexistence of various data transmission without interfering with each other and stable operation of the system. In view of 5G uplink transmission in industrial transmission scenarios, a prediction-based resource allocation scheme is proposed, which uses Auto Regressive Moving Average (ARMA) model to predict the activation rates of the next transmission cycle based on the historical data. Then the resources are dynamically reserved for periodic and emergency data, so as to minimize the impact on periodic data transmission under the premise of meeting the emergency data transmission conditions. Experimental results show that, compared with the traditional resource allocation scheme, this scheme can effectively reduce the impact of emergency data transmission on periodic data and improve the utilization of spectrum resources.
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表 1 仿真参数设置
仿真参数 参数值 数据包大小$B$ 100 bit 紧急设备数量${N_{\rm{s}}}$ 30 周期设备数量${N_{\rm{p}}}$ 22 总带宽$W$ 15 MHz 子载波间隔$\varpi $ 15 kHz 最小传输时间间隔$\tau $ 0.144 ms(2个符号长度) ACK时间间隔${t_{{\rm{ack}}}}$ 0.288 ms 延迟限制$T$ 1 ms 丢失率限制${P_{\rm{f}}}$ 10–5 重传数据包数量$\beta $ 3 
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