Research on Prediction Based Emergency Resource Allocation in 5G Uplink

XU Fangmin; WU Lijiao; WANG Xiang; ZHAO Chenglin

doi:10.11999/JEIT201050

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 611-619

XU Fangmin, WU Lijiao, WANG Xiang, ZHAO Chenglin. Research on Prediction Based Emergency Resource Allocation in 5G Uplink[J]. Journal of Electronics & Information Technology, 2022, 44(2): 611-619. doi: 10.11999/JEIT201050

Citation:

XU Fangmin, WU Lijiao, WANG Xiang, ZHAO Chenglin. Research on Prediction Based Emergency Resource Allocation in 5G Uplink[J]. Journal of Electronics & Information Technology, 2022, 44(2): 611-619. doi: 10.11999/JEIT201050

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Research on Prediction Based Emergency Resource Allocation in 5G Uplink

doi: 10.11999/JEIT201050

1.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
Beijing Jianyi Technology Co., Ltd, Beijing 100086, China

Funds: The 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks”, The Beijing Natural Science Foundation - Haidian Frontier Project Research on Key Technologies of Wireless Edge Intelligent Collaboration for Industrial Internet Scenarios (L202017)

Received Date: 2020-12-14
Rev Recd Date: 2021-05-18

Available Online: 2021-06-03

Publish Date: 2022-02-25

Abstract

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.
- Resource allocation,
- 5G,
- Auto Regressive Moving Average (ARMA),
- Grant-free transmission,
- Uplink transmission

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References(18)

References

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