Data Transmission of Digital Grid Assisted by Intelligent Relaying
Keywords:Digital grid, data transmission, outage probability, analytical expression, asymptotic expression
In this paper, we study the relaying and cache aided digital grid data transmission, where the relaying may be equipped by caching or not, depending on specific applications. For both cases, we evaluate the impact of relaying and caching on the system performance of digital grid data transmission through theoretical derivation. To this end, an analytical expression on the outage probability is firstly derived for the data transmission. We then provide an asymptotic expression on the system outage probability. Finally, some simulation results are provided to verify the correctness of the derived analysis on the system performance, and show the impact of relaying and caching on the data transmission of digital grid system. In particular, the usage of caching at the relaying can help strengthen the data transmission performance of the considered system effectively. The results in this paper could provide some reference to the development of wireless transmission and scalable information systems.
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.
B. Wang, F. Gao, S. Jin, H. Lin, and G. Y. Li, “Spatial- and frequency-wideband effects in millimeter-wave massive MIMO systems,” IEEE Trans. Signal Processing, vol. 66, no. 13, pp. 3393–3406, 2018.
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
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.
X. Hu, J. Wang, and C. Zhong, “Statistical CSI based design for intelligent reflecting surface assisted MISO systems,” Science China: Information Science, vol. 63, no. 12, p. 222303, 2020.
X. Lai, “Outdated access point selection for mobile edge computing with cochannel interference,” IEEE Trans.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. 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.
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.
L. Zhang and S. Tang, “Scoring aided federated learning on long-tailed data for time-varying IoMT based healthcare system,” IEEE Journal of Biomedical and Health Informatics, vol. PP, no. 99, pp. 1–12, 2023.
J. Lu and M. Tang, “IRS-UAV aided mobile edge computing networks with constrained latency: Analysis and optimization,” Physical Communication, vol. 2023, p. 101869, 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.
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.
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.
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.
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.
S. Tang, “Dilated convolution based CSI feedback compression for massive MIMO systems,” IEEE Trans. Vehic. Tech., vol. 71, no. 5, pp. 211–216, 2022.
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.
R. Zhao and M. Tang, “Profit maximization in cache-aided intelligent computing networks,” Physical Commu-nication, vol. PP, no. 99, pp. 1–10, 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.
L. Chen, “Physical-layer security on mobile edge computing for emerging cyber physical systems,” Computer Communications, vol. 194, no. 1, pp. 180–188, 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.
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.
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. Lu and M. Tang, “Performance analysis for IRS-assisted MEC networks with unit selection,” Physical Communication, vol. 55, p. 101869, 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.
W. Wu, F. Zhou, R. Q. Hu, and B. Wang, “Energy-efficient resource allocation for secure noma-enabled mobile edge computing networks,” IEEE Trans. Commun., vol. 68, no. 1, pp. 493–505, 2020.
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. 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.
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Copyright (c) 2022 Shuangbai He, Chun Yang, Yuda Li, Binyu Xie, Jiaqi Zhao
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