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Green Operations of SWIPT Networks: The Role of End-User Devices (2312.08232v3)

Published 13 Dec 2023 in cs.NI and eess.SP

Abstract: Internet of Things (IoT) devices often come with batteries of limited capacity that are not easily replaceable or rechargeable, and that constrain significantly the sensing, computing, and communication tasks that they can perform. The Simultaneous Wireless Information and Power Transfer (SWIPT) paradigm addresses this issue by delivering power wirelessly to energy-harvesting IoT devices with the same signal used for information transfer. For their peculiarity, these networks require specific energy-efficient planning and management approaches. However, to date, it is not clear what are the most effective strategies for managing a SWIPT network for energy efficiency. In this paper, we address this issue by developing an analytical model based on stochastic geometry, accounting for the statistics of user-perceived performance and base station scheduling. We formulate an optimization problem for deriving the energy optimal configuration as a function of the main system parameters, and we propose a genetic algorithm approach to solve it. Our results enable a first-order evaluation of the most effective strategies for energy-efficient provisioning of power and communications in a SWIPT network. We show that the service capacity brought about by users brings energy-efficient dynamic network provisioning strategies that radically differ from those of networks with no wireless power transfer.

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References (39)
  1. G. Rizzo, M. A. Marsan, and C. Esposito, “Energy-optimal ran configurations for swipt iot,” in 2022 20th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2022, pp. 169–176.
  2. “Prepare yourself for the “tsunami of data” expected to hit by 2025,” https://futurism.com/prepare-yourself-tsunami-data-expected-hit-2025, (Accessed on 05/09/2022).
  3. H. Tahaei, F. Afifi, A. Asemi, F. Zaki, and N. B. Anuar, “The rise of traffic classification in iot networks: A survey,” Journal of Network and Computer Applications, vol. 154, p. 102538, 2020.
  4. Y. Wan, K. Xu, F. Wang, and G. Xue, “Characterizing and mining traffic patterns of iot devices in edge networks,” IEEE Transactions on Network Science and Engineering, vol. 8, no. 1, pp. 89–101, 2021.
  5. T. Sanislav, G. D. Mois, S. Zeadally, and S. C. Folea, “Energy harvesting techniques for internet of things (iot),” IEEE Access, vol. 9, pp. 39 530–39 549, 2021.
  6. L. R. Varshney, “Transporting information and energy simultaneously,” in 2008 IEEE international symposium on information theory.   IEEE, 2008, pp. 1612–1616.
  7. A. Costanzo, D. Masotti, G. Paolini, and D. Schreurs, “Evolution of swipt for the iot world: Near-and far-field solutions for simultaneous wireless information and power transfer,” IEEE Microwave Magazine, vol. 22, no. 12, pp. 48–59, 2021.
  8. J. Huang, C.-C. Xing, and C. Wang, “Simultaneous wireless information and power transfer: Technologies, applications, and research challenges,” IEEE Communications Magazine, vol. 55, no. 11, pp. 26–32, 2017.
  9. S. Özyurt, A. Coşkun, S. Büyükçorak, G. K. Kurt, and O. Kucur, “A survey on multiuser swipt communications for 5g+,” IEEE Access, vol. 10, pp. 109 814–109 849, 2022.
  10. T. D. P. Perera, D. N. K. Jayakody, S. K. Sharma, S. Chatzinotas, and J. Li, “Simultaneous wireless information and power transfer (swipt): Recent advances and future challenges,” IEEE Communications Surveys & Tutorials, vol. 20, no. 1, pp. 264–302, 2017.
  11. X. Zhou, R. Zhang, and C. K. Ho, “Wireless Information and Power Transfer: Architecture Design and Rate-Energy Tradeoff,” IEEE Trans. Commun., vol. 61, no. 11, pp. 4754–4767, Nov. 2013.
  12. Y. Huang, M. Liu, and Y. Liu, “Energy-Efficient SWIPT in IoT Distributed Antenna Systems,” IEEE Internet Things J., vol. 5, no. 4, pp. 2646–2656, Aug. 2018.
  13. H.-V. Tran, G. Kaddoum, and K. T. Truong, “Resource allocation in swipt networks under a nonlinear energy harvesting model: Power efficiency, user fairness, and channel nonreciprocity,” IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8466–8480, 2018.
  14. W. Lu, X. Xu, G. Huang, B. Li, Y. Wu, N. Zhao, and F. R. Yu, “Energy Efficiency Optimization in SWIPT Enabled WSNs for Smart Agriculture,” IEEE Trans. Ind. Inf., vol. 17, no. 6, pp. 4335–4344, Jun. 2021.
  15. K. Lee and W. Lee, “Learning-based resource management for swipt,” IEEE Systems Journal, vol. 14, no. 4, pp. 4750–4753, 2020.
  16. T. T. Lam, M. Di Renzo, and J. P. Coon, “System-level analysis of swipt mimo cellular networks,” IEEE Communications Letters, vol. 20, no. 10, pp. 2011–2014, 2016.
  17. M. Di Renzo, “System-Level Analysis and Optimization of Cellular Networks With Simultaneous Wireless Information and Power Transfer: Stochastic Geometry Modeling,” IEEE Trans. Veh. Technol., vol. 66, no. 3, pp. 2251–2275, Mar. 2017.
  18. F. Baccelli, B. Błaszczyszyn et al., “Stochastic geometry and wireless networks: Volume II Applications,” Foundations and Trends in Networking, vol. 4, no. 1–2, pp. 1–312, 2010.
  19. D. Niyato, D. I. Kim, M. Maso, and Z. Han, “Wireless powered communication networks: Research directions and technological approaches,” IEEE Wireless Communications, vol. 24, no. 6, pp. 88–97, 2017.
  20. B. Clerckx, R. Zhang, R. Schober, D. W. K. Ng, D. I. Kim, and H. V. Poor, “Fundamentals of wireless information and power transfer: From rf energy harvester models to signal and system designs,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 1, pp. 4–33, 2019.
  21. Y. Luo, C. Luo, G. Min, G. Parr, and S. McClean, “On the Study of Sustainability and Outage of SWIPT-Enabled Wireless Communications,” IEEE J. Sel. Top. Signal Process., vol. 15, no. 5, pp. 1159–1168, Aug. 2021.
  22. J. Tang, D. K. C. So, N. Zhao, A. Shojaeifard, and K.-K. Wong, “Energy Efficiency Optimization With SWIPT in MIMO Broadcast Channels for Internet of Things,” IEEE Internet Things J., vol. 5, no. 4, pp. 2605–2619, Aug. 2018.
  23. J. Tang, J. Luo, M. Liu, D. K. C. So, E. Alsusa, G. Chen, K.-K. Wong, and J. A. Chambers, “Energy Efficiency Optimization for NOMA With SWIPT,” IEEE J. Sel. Top. Signal Process., vol. 13, no. 3, pp. 452–466, Jun. 2019.
  24. Y. Yuan, Y. Xu, Z. Yang, P. Xu, and Z. Ding, “Energy Efficiency Optimization in Full-Duplex User-Aided Cooperative SWIPT NOMA Systems,” IEEE Trans. Commun., vol. 67, no. 8, pp. 5753–5767, Aug. 2019.
  25. Q. Li and L. Yang, “Robust Optimization for Energy Efficiency in MIMO Two-Way Relay Networks With SWIPT,” IEEE Systems Journal, vol. 14, no. 1, pp. 196–207, Mar. 2020.
  26. M. Chu, A. Liu, J. Chen, V. K. N. Lau, and S. Cui, “A stochastic geometry analysis for energy-harvesting-based device-to-device communication,” IEEE Internet of Things Journal, vol. 9, no. 2, pp. 1591–1607, 2022.
  27. A. A. Nasir, X. Zhou, S. Durrani, and R. A. Kennedy, “Relaying protocols for wireless energy harvesting and information processing,” IEEE Transactions on Wireless Communications, vol. 12, no. 7, pp. 3622–3636, 2013.
  28. S. Wang, M. Xia, K. Huang, and Y.-C. Wu, “Wirelessly powered two-way communication with nonlinear energy harvesting model: Rate regions under fixed and mobile relay,” IEEE Transactions on Wireless Communications, vol. 16, no. 12, pp. 8190–8204, 2017.
  29. B. Debaillie, C. Desset, and F. Louagie, “A flexible and future-proof power model for cellular base stations,” in Vehicular Technology Conference (VTC Spring), 81st.   IEEE, 2015, pp. 1–7.
  30. M. S. Mushtaq, S. Fowler, and A. Mellouk, “Power saving model for mobile device and virtual base station in the 5G era,” in IEEE ICC, Paris, France, May 2017, pp. 1–6.
  31. M. Banafaa, I. Shayea, J. Din, M. Hadri Azmi, A. Alashbi, Y. Ibrahim Daradkeh, and A. Alhammadi, “6g mobile communication technology: Requirements, targets, applications, challenges, advantages, and opportunities,” Alexandria Engineering Journal, vol. 64, pp. 245–274, 2023.
  32. Z. Qadir, K. N. Le, N. Saeed, and H. S. Munawar, “Towards 6g internet of things: Recent advances, use cases, and open challenges,” ICT Express, vol. 9, no. 3, pp. 296–312, 2023.
  33. F. Guo, F. R. Yu, H. Zhang, X. Li, H. Ji, and V. C. M. Leung, “Enabling massive iot toward 6g: A comprehensive survey,” IEEE Internet of Things Journal, vol. 8, no. 15, pp. 11 891–11 915, 2021.
  34. D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, D. Niyato, O. Dobre, and H. V. Poor, “6g internet of things: A comprehensive survey,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 359–383, 2022.
  35. K. Rajwar, K. Deep, and S. Das, “An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges,” Artificial Intelligence Review, pp. 1–71, 2023.
  36. J. Lin, Y. Chen, H. Zheng, M. Ding, P. Cheng, and L. Hanzo, “A data-driven base station sleeping strategy based on traffic prediction,” IEEE Transactions on Network Science and Engineering, 2021.
  37. X. Ge, J. Yang, H. Gharavi, and Y. Sun, “Energy efficiency challenges of 5g small cell networks,” IEEE communications Magazine, vol. 55, no. 5, pp. 184–191, 2017.
  38. G. A. Rizzo and M. A. Marsan, “The energy saving potential of static and adaptive resource provisioning in dense cellular networks,” in IEEE COMSNETS, 2018, pp. 297–304.
  39. B. Rengarajan, G. Rizzo, and M. A. Marsan, “Energy-optimal base station density in cellular access networks with sleep modes,” Computer Networks, vol. 78, pp. 152–163, 2015.
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