TY - GEN
T1 - Optimizing Training and Transmission Overheads of URLLC in Industrial IoT Networks
AU - Xie, Yuncong
AU - Ren, Pinyi
AU - Xu, Dongyang
AU - Li, Qiang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - In this paper, we focus on the uplink transmission of URLLC in Industrial IoT(IIoT) Networks, and investigate the impact of channel estimation accuracy on the transmission performance and coverage range of URLLC networks. Also, we aim to find an optimal tradeoff between channel training and data transmission overheads in the short blocklength regime. Firstly, we derive a closed-form approximation of the transmission error probability in URLLC with imperfect CSI, which is a function with respect to the training pilot length and data transmission duration. Secondly, the number of symbols allocated to the channel training and data transmission phases are jointly optimized to maximize the available range of URLLC in the IIoT network, which is defined as the maximal communication range that the QoS requirement of URLLC can be satisfied. Moreover, the impact of spatial diversity on the transmission performance and coverage range is also analyzed. Simulation results validate the accuracy of theoretical analyses, and demonstrate that an appropriate tradeoff between channel training and data transmission overheads is helpful to improve the transmission performance and coverage range of URLLC in IIoT networks.
AB - In this paper, we focus on the uplink transmission of URLLC in Industrial IoT(IIoT) Networks, and investigate the impact of channel estimation accuracy on the transmission performance and coverage range of URLLC networks. Also, we aim to find an optimal tradeoff between channel training and data transmission overheads in the short blocklength regime. Firstly, we derive a closed-form approximation of the transmission error probability in URLLC with imperfect CSI, which is a function with respect to the training pilot length and data transmission duration. Secondly, the number of symbols allocated to the channel training and data transmission phases are jointly optimized to maximize the available range of URLLC in the IIoT network, which is defined as the maximal communication range that the QoS requirement of URLLC can be satisfied. Moreover, the impact of spatial diversity on the transmission performance and coverage range is also analyzed. Simulation results validate the accuracy of theoretical analyses, and demonstrate that an appropriate tradeoff between channel training and data transmission overheads is helpful to improve the transmission performance and coverage range of URLLC in IIoT networks.
KW - Industrial Internet-of-Things (IIoT)
KW - Ultra-reliable and low-latency communications (URLLC)
KW - channel training
KW - transmission performance optimization
UR - https://www.scopus.com/pages/publications/85102920056
U2 - 10.1109/GCWkshps50303.2020.9367447
DO - 10.1109/GCWkshps50303.2020.9367447
M3 - 会议稿件
AN - SCOPUS:85102920056
T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Globecom Workshops, GC Wkshps 2020
Y2 - 7 December 2020 through 11 December 2020
ER -