Research on Communication Technology of OPGW Line in Distribution Network under Interference Environment

Bo Li; Meiqin Huang; Huanyu Zhang; Mi Lin; Shuyi He; Liming Chen

doi:10.4108/eetsis.v10i3.2780

Authors

Bo Li Hainan Power Grid Co., Ltd, China
Meiqin Huang Information and Communication Branch of Hainan Power Grid, China
Huanyu Zhang Hainan Power Grid Co., Ltd, China
Mi Lin Hainan Power Grid Co., Ltd, China
Shuyi He Information and Communication Branch of Hainan Power Grid, China
Liming Chen China Southern Power Grid Research Institute Co., Ltd, China

DOI:

https://doi.org/10.4108/eetsis.v10i3.2780

Keywords:

Digital grid, data transmission, outage probability, analytical expression, OPGW communication

Abstract

In optical fiber composite overhead ground wire (OPGW) networks, the current monitoring is mainly through installing electronic sensors on the cable and manually monitoring the video on the cable, where the interference plays an important role in the communication and monitoring based systems. In essence, the interference arises from aggressive frequency reuse, especially in the frequency-limited Internet of Things (IoT) networks. The existence of interference causes a negative effect on the system performance of communication systems and IoT networks including the OPGW networks. Hence, this article investigates the communication technology of OPGW line in distribution network under interference environment, where there is one primary link, one secondary link, and one legitimate monitor listening to the secondary link. The secondary user needs to transmit its message to the secondary receiver under the interference power constrained by the primary node. We firstly define the outage probability of legitimate monitoring based on the data rate, and then analyze the system performance by theoretically deriving a closed-form expression of the outage probability for the OPGW communication under interference environment. Simulation results are finally demonstrated to verify the correctness of the closed-form expression for the OPGW communication under interference environment, and show that the interference has a negative impact on the OPGW communication performance.

References

H. Wang and Z. Huang, “Guest editorial: WWWJ special issue of the 21th international conference on web information systems engineering (WISE 2020),” World Wide Web, vol. 25, no. 1, pp. 305–308, 2022.

H. Wang, J. Cao, and Y. Zhang, Access Control Management in Cloud Environments. Springer, 2020. [Online]. Available: https://doi.org/10.1007/978-3-030-31729-4

X. Lai, “Outdated access point selection for mobile edge computing with cochannel interference,” IEEE Trans. Vehic. Tech., vol. 71, no. 7, pp. 7445–7455, 2022.

L. Zhang and C. Gao, “Deep reinforcement learning based IRS-assisted mobile edge computing under physical-layer security,” Physical Communication, vol. 55, p. 101896, 2022.

N. Dahlin and R. Jain, “Scheduling flexible nonpreemp-tive loads in smart-grid networks,” IEEE Trans. Control. Netw. Syst., vol. 9, no. 1, pp. 14–24, 2022.

E. Z. Serper and A. Altin-Kayhan, “Coverage and connectivity based lifetime maximization with topology update for WSN in smart grid applications,” Comput. Networks, vol. 209, p. 108940, 2022.

Z. Alavikia and M. Shabro, “A comprehensive layered approach for implementing internet of things-enabled smart grid: A survey,” Digit. Commun. Networks, vol. 8, no. 3, pp. 388–410, 2022.

S. Mishra, “Blockchain-based security in smart grid network,” Int. J. Commun. Networks Distributed Syst., vol. 28, no. 4, pp. 365–388, 2022.

Z. Na, C. Ji, B. Lin, and N. Zhang, “Joint optimization of trajectory and resource allocation in secure uav relaying communications for internet of things,” IEEE Internet of Things Journal, 2022.

L. Chen and X. Lei, “Relay-assisted federated edge learn-ing:Performance analysis and system optimization,” IEEE Transactions on Communications, vol. PP, no. 99, pp. 1–12, 2022.

Y. Wu and C. Gao, “Intelligent resource allocation scheme for cloud-edge-end framework aided multi-source data stream,” to appear in EURASIP J. Adv. Signal Process., vol. 2023, no. 1, 2023.

B. Li, Z. Na, and B. Lin, “Uav trajectory planning from a comprehensive energy efficiency perspective in harsh environments,” IEEE Network, vol. 36, no. 4, pp. 62–68, 2022.

W. Zhou and X. Lei, “Priority-aware resource scheduling for uav-mounted mobile edge computing networks,” IEEE Trans. Vehic. Tech., vol. PP, no. 99, pp. 1–6, 2023.

J. Lu and M. Tang, “Performance analysis for IRS-assisted MEC networks with unit selection,” Physical Communication, vol. 55, p. 101869, 2022.

W. Wu, F. Zhou, B. Wang, Q. Wu, C. Dong, and R. Q. Hu, “Unmanned aerial vehicle swarm-enabled edge computing: Potentials, promising technologies, and challenges,” IEEE Wirel. Commun., vol. 29, no. 4, pp. 78–85, 2022.

L. He and X. Tang, “Learning-based MIMO detection with dynamic spatial modulation,” IEEE Transactions on Cognitive Communications and Networking, vol. PP, no. 99, pp. 1–12, 2023.

Y. Wu and C. Gao, “Task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach,” Physical Communication, vol. 55, p. 101867, 2022.

X. Zheng and C. Gao, “Intelligent computing for WPT-MEC aided multi-source data stream,” to appear in EURASIP J. Adv. Signal Process., vol. 2023, no. 1, 2023.

S. Tang and X. Lei, “Collaborative cache-aided relaying networks: Performance evaluation and system optimiza-tion,” IEEE Journal on Selected Areas in Communications, vol. PP, no. 99, pp. 1–12, 2022.

Y. Guo and W. Xu, “Resource allocation in wireless power transfer assisted federated learning networks,” IEEE Transactions on Communications, vol. PP, no. 99, pp. 1–12, 2023.

L. Chen, “Physical-layer security on mobile edge computing for emerging cyber physical systems,” Computer Communications, vol. 194, no. 1, pp. 180–188, 2022.

W. Wu, Z. Wang, L. Yuan, F. Zhou, F. Lang, B. Wang, and Q. Wu, “Irs-enhanced energy detection for spectrum sensing in cognitive radio networks,” IEEE Wirel. Commun. Lett., vol. 10, no. 10, pp. 2254–2258, 2021.

J. Ling and C. Gao, “Dqn based resource allocation for NOMA-MEC aided multi-source data stream,” to appear in EURASIP J. Adv. Signal Process., vol. 2023, no. 1, 2023.

S. Tang and L. Chen, “Computational intelligence and deep learning for next-generation edge-enabled industrial IoT,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 3, pp. 105–117, 2022.

S. Tang, “Dilated convolution based CSI feedback compression for massive MIMO systems,” IEEE Trans. Vehic. Tech., vol. 71, no. 5, pp. 211–216, 2022.

R. Zhao and M. Tang, “Profit maximization in cache-aided intelligent computing networks,” Physical Commu-nication, vol. PP, no. 99, pp. 1–10, 2022.

W. Zhou and F. Zhou, “Profit maximization for cache-enabled vehicular mobile edge computing networks,” IEEE Trans. Vehic. Tech., vol. PP, no. 99, pp. 1–6, 2023.

R. Zhao, C. Fan, J. Ou, D. Fan, J. Ou, and M. Tang, “Impact of direct links on intelligent reflect surface-aided mec networks,” Physical Communication, vol. 55, p. 101905, 2022.

J. Liu, T. Cui, L. Zhang, and Y. Zhang, “Deep federated learning based convergence analysis in relaying-aided MEC-IoT networks,” Journal of Engineering, vol. 2022, no. 8, pp. 101–108, 2022.

Y. Tang and S. Lai, “Intelligent distributed data storage for wireless communications in b5g networks,” EAI Endorsed Transactions on Mobile Communications and Applications, vol. 2022, no. 8, pp. 121–128, 2022.