An Overview on Active Transmission Techniques for Wireless Scalable Networks
DOI:
https://doi.org/10.4108/eetsis.v9i6.2419Keywords:
Active transmission, latency, data rate, energy consumptionAbstract
Currently, massive data communication and computing pose a severe challenge on existing wireless network architecture, from various aspects such as data rate, latency, energy consumption and pricing. Hence, it is of vital importance to investigate active wireless transmission for wireless networks. To this end, we first overview the data rate of wireless active transmission. We then overview the latency of wireless active transmission, which is particularly important for the applications of monitoring services. We further overview the spectral efficiency of the active transmission, which is particularly important for the battery-limited Internet of Things (IoT) networks. After these overviews, we give several critical challenges on the active transmission, and we finally present feasible solutions to meet these challenges. The work in this paper can serve as an important reference to the wireless networks and IoT networks.
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.
Y. Guo and S. Lai, “Distributed machine learning for multiuser mobile edge computing systems,” IEEE J. Sel. Top. Signal Process., vol. 16, no. 3, pp. 460–473, 2022.
L. He, K. He, L. Fan, X. Lei, A. Nallanathan, and G. K. Karagiannidis, “Toward optimally efficient search with deep learning for large-scale MIMO systems,” IEEE Trans. Commun., vol. 70, no. 5, pp. 3157–3168, 2022.
X. Lai, “Outdated access point selection for mobile edge computing with cochannel interference,” IEEE Trans.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.
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
K. He and Y. Deng, “Efficient memory-bounded optimal detection for GSM-MIMO systems,” IEEE Trans. Commun., vol. 70, no. 7, pp. 4359–4372, 2022.
J. Lu, “Analytical offloading design for mobile edge computing based smart internet of vehicle,” EURASIP J. Adv. Signal Process., vol. 2022, no. 1.
L. Zhang, “DQN based mobile edge computing for smart internet of vehicle,” EURASIP J. Adv. Signal Process., vol. 2022, no. 1.
H. Wang, Y. Wang, T. Taleb, and X. Jiang, “Editorial: Special issue on security and privacy in network computing,” World Wide Web, vol. 23, no. 2, pp. 951–957, 2020.
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.
L. Chen, “Physical-layer security on mobile edge computing for emerging cyber physical systems,” Computer Communications, vol. PP, no. 99, pp. 1–12, 2022.
J. Sun, X. Wang, Y. Fang, X. Tian, M. Zhu, J. Ou, and C. Fan, “Security performance analysis of relay networks based on-shadowed channels with rhis and cees,” Wireless Communications and Mobile Computing, vol. 2022, 2022.
X. Deng, S. Zeng, L. Chang, Y. Wang, X. Wu, J. Liang,
J. Ou, and C. Fan, “An ant colony optimization-based routing algorithm for load balancing in leo satellite networks,” Wireless Communications and Mobile Computing, vol. 2022, 2022.
C. Wang, W. Yu, F. Zhu, J. Ou, C. Fan, J. Ou, and D. Fan, “Uav-aided multiuser mobile edge computing networks with energy harvesting,” Wireless Communications and Mobile Computing, vol. 2022, 2022.
J. Chen, Y. Wang, J. Ou, C. Fan, X. Lu, C. Liao,
X. Huang, and H. Zhang, “Albrl: Automatic load-balancing architecture based on reinforcement learning in software-defined networking,” Wireless Communica-tions and Mobile Computing, vol. 2022, 2022.
C. Ge, Y. Rao, J. Ou, C. Fan, J. Ou, and D. Fan, “Joint offloading design and bandwidth allocation for ris-aided multiuser mec networks,” Physical Communication, p. 101752, 2022.
C. Yang, B. Song, Y. Ding, J. Ou, and C. Fan, “Efficient data integrity auditing supporting provable data update for secure cloud storage,” Wireless Communications and Mobile Computing, vol. 2022, 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.
——, “Impact of direct links on intelligent reflect surface-aided MEC networks,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.
J. Liu, Y. Zhang, J. Wang, T. Cui, L. Zhang, C. Li, K. Chen, S. Li, S. Feng, D. Xie et al., “Outage probability analysis for uav-aided mobile edge computing networks,” EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 9, no. 31, pp. e4–e4, 2022.
L. Zhang and C. Gao, “Deep reinforcement learning based IRS-assisted mobile edge computing underphysical-layer security,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.
J. Lu and M. Tang, “Performance analysis for IRS-assisted MEC networks with unit selection,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.
Y. Wu and C. Gao, “Intelligent task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.
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.
J. Liu, Y. Zhang, J. Wang, T. Cui, L. Zhang, C. Li, K. Chen, H. Huang, X. Zhou, W. Zhou et al., “The intelligent bi-directional relaying communication for edge intelligence based industrial iot networks: Intelligent bi-directional relaying communication,” EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 9, no. 32, pp. e4–e4, 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.
——, “Energy-efficient and high-spectrum-efficiency wireless transmission,” EAI Endorsed Transactions on Mobile Communications and Applications, vol. 2022, no. 8, pp. 129–135, 2022.
J. Liu and W. Zhou, “Deep model training and deployment on scalable iot networks: A survery,” EAI Endorsed Transactions on Scalable Information Systems, vol. 2022, no. 2, pp. 29–35, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Yajuan Tang, Shiwei Lai, Zichao Zhao, Yanyi Rao, Wen Zhou, Fusheng Zhu, Liming Chen, Dan Deng, Jing Wang, Tao Cui, Yuwei Zhang, Jun Liu, Di Wu, Huang Huang, Xuan Zhou, Wei Zhou, Zhao Wang, Kai Chen, Chao Li, Yun Li, Kaimeno Dube, Abbarbas Muazu, Nakilavai Rono, Suili Feng, Jiayin Qin, Haige Xiang, Zhigang Cao, Lieguang Zeng, Zhixing Yang, Zhi Wang, Yan Xu, Xiaosheng Lin, Zizhi Wang, Yu Zhang, Bowen Lu, Wanxin Zou
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
