Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Energy Efficient Design of Active STAR-RIS-Aided SWIPT Systems (2403.15754v1)

Published 23 Mar 2024 in cs.IT, eess.SP, and math.IT

Abstract: In this paper, we consider the downlink transmission of a multi-antenna base station (BS) supported by an active simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS) to serve single-antenna users via simultaneous wireless information and power transfer (SWIPT). In this context, we formulate an energy efficiency maximisation problem that jointly optimises the gain, element selection and phase shift matrices of the active STAR-RIS, the transmit beamforming of the BS and the power splitting ratio of the users. With respect to the highly coupled and non-convex form of this problem, an alternating optimisation solution approach is proposed, using tools from convex optimisation and reinforcement learning. Specifically, semi-definite relaxation (SDR), difference of concave functions (DC), and fractional programming techniques are employed to transform the non-convex optimisation problem into a convex form for optimising the BS beamforming vector and the power splitting ratio of the SWIPT. Then, by integrating meta-learning with the modified deep deterministic policy gradient (DDPG) and soft actor-critical (SAC) methods, a combinatorial reinforcement learning network is developed to optimise the element selection, gain and phase shift matrices of the active STAR-RIS. Our simulations show the effectiveness of the proposed resource allocation scheme. Furthermore, our proposed active STAR-RIS-based SWIPT system outperforms its passive counterpart by 57% on average.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. N. Garg and R. Garg, “Energy harvesting in IoT devices: A survey,” in Proc. International Conference on Intelligent Sustainable Systems (ICISS).   IEEE, 2017, pp. 127–131.
  2. Q. Wu, X. Guan, and R. Zhang, “Intelligent reflecting surface-aided wireless energy and information transmission: An overview,” Proc. IEEE, vol. 110, no. 1, pp. 150–170, 2021.
  3. 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 J. Sel. Areas Commun., vol. 37, no. 1, pp. 4–33, 2018.
  4. E. Soleimani-Nasab and S. Javadi, “Performance analysis of two way wireless powered amplify and forward relaying in the presence of co-channel interference,” IEEE Commun. Surv. Tutor., vol. 34, no. 1, pp. 2047–2077, 2021.
  5. H. Zarini, N. Gholipoor, M. R. Mili, M. Rasti, H. Tabassum, and E. Hossain, “Resource management for multiplexing eMBB and URLLC services over RIS-aided THz communication,” IEEE Trans. Commun., vol. 71, no. 2, pp. 1207–1225, 2023.
  6. D. M. Mughal, D. Munir, and M. Y. Chung, “Outage analysis of IRS-assisted RF powered networks for energy-constrained IoT devices,” IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 7805–7818, 2023.
  7. S. Javadi, S. Faramarzi, F. Zeinali, H. Zarini, M. R. Mili, P. D. Diamantoulakis, E. Jorswieck, and G. K. Karagiannidis, “SLIPT in joint dimming multi-LED OWC systems with rate splitting multiple access,” arXiv preprint arXiv:2402.16629, 2024.
  8. H. Zarini, N. Gholipoor, M. R. Mili, M. Rasti, H. Tabassum, and E. Hossain, “Liquid state machine-empowered reflection tracking in RIS-aided THz communications,” in Proc. IEEE Global Communications Conference (GLOBECOM).   IEEE, 2022, pp. 5273–5278.
  9. Q. Wu and R. Zhang, “Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network,” IEEE Commun. Mag., vol. 58, no. 1, pp. 106–112, 2019.
  10. Z. Zhang, L. Dai, X. Chen, C. Liu, F. Yang, R. Schober, and H. V. Poor, “Active RIS vs. passive RIS: Which will prevail in 6666G?” IEEE Trans. Commun., 2022.
  11. P. S. Aung, Y. M. Park, Y. K. Tun, Z. Han, and C. S. Hong, “Energy-efficient communication networks via multiple aerial reconfigurable intelligent surfaces: DRL and optimization approach,” arXiv preprint arXiv:2207.03149, 2022.
  12. J.-C. Chen, “Energy-efficient hybrid beamforming design for intelligent reflecting surface-assisted mmwave massive MU-MISO systems,” IEEE trans. green commun. netw., 2023.
  13. Y. Liu, X. Mu, J. Xu, R. Schober, Y. Hao, H. V. Poor, and L. Hanzo, “STAR: Simultaneous transmission and reflection for 360° coverage by intelligent surfaces,” IEEE Wirel. Commun., vol. 28, no. 6, pp. 102–109, 2021.
  14. M. Ahmed, A. Wahid, S. S. Laique, W. U. Khan, A. Ihsan, F. Xu, S. Chatzinotas, and Z. Han, “A survey on STAR-RIS: Use cases, recent advances, and future research challenges,” IEEE Internet Things J., 2023.
  15. C. Zhou, B. Lyu, S. Gong, and C. You, “Active STAR-RIS assisted symbiotic radio communications under hardware impairments,” IEEE Commun. Lett., 2023.
  16. F. Fang, B. Wu, S. Fu, Z. Ding, and X. Wang, “Energy-efficient design of STAR-RIS aided MIMO-NOMA networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 498–511, 2022.
  17. X. Ma, X. Lei, P. T. Mathiopoulos, and D. B. da Costa, “Active STAR-RIS aided cell-free massive MIMO: A performance study,” IEEE Trans. Veh. Technol., 2023.
  18. J. Chen, Z. Ma, Y. Zou, J. Jia, and X. Wang, “DRL-based energy efficient resource allocation for STAR-RIS assisted coordinated multi-cell networks,” in Proc. Global Communications Conference (GLOBECOM).   IEEE, 2022, pp. 4232–4237.
  19. Y. Guo, F. Fang, D. Cai, and Z. Ding, “Energy-efficient design for a NOMA assisted STAR-RIS network with deep reinforcement learning,” IEEE Trans. Veh. Technol., vol. 72, no. 4, pp. 5424–5428, 2022.
  20. Y. Peng, J. Tang, Q. Yang, Z. Han, and J. Ma, “Joint power allocation algorithm for UAV-borne simultaneous transmitting and reflecting reconfigurable intelligent surface-assisted non-orthogonal multiple access system,” IEEE Access, pp. 1–1, 2023.
  21. Q. Zhang, Y. Zhao, H. Li, S. Hou, and Z. Song, “Joint optimization of STAR-RIS assisted UAV communication systems,” IEEE Commun. Lett., vol. 11, no. 11, pp. 2390–2394, 2022.
  22. S. Faramarzi, S. Javadi, F. Zeinali, H. Zarini, M. R. Mili, M. Bennis, Y. Li, and K.-K. Wong, “Meta reinforcement learning for resource allocation in aerial active-RIS-assisted networks with rate-splitting multiple access,” arXiv preprint arXiv:2403.08648, 2024.
  23. P. Zhao, J. Zuo, and C. Wen, “Power allocation and beamforming vectors optimization in STAR-RIS assisted SWIPT,” in International Conference on Communication Technology (ICCT).   IEEE, 2022, pp. 1174–1178.
  24. T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971, 2015.
  25. T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel et al., “Soft actor-critic algorithms and applications,” arXiv preprint arXiv:1812.05905, 2018.
  26. C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. International conference on machine learning.   PMLR, 2017, pp. 1126–1135.
  27. C. Wu, C. You, Y. Liu, X. Gu, and Y. Cai, “Channel estimation for star-ris-aided wireless communication,” IEEE Communications Letters, vol. 26, no. 3, pp. 652–656, 2021.
  28. Y. Liu, X. Mu, J. Xu, R. Schober, Y. Hao, H. V. Poor, and L. Hanzo, “Star: Simultaneous transmission and reflection for 360° coverage by intelligent surfaces,” IEEE Wireless Communications, vol. 28, no. 6, pp. 102–109, 2021.
  29. X. Mu, Y. Liu, L. Guo, J. Lin, and R. Schober, “Simultaneously transmitting and reflecting (star) ris aided wireless communications,” IEEE Transactions on Wireless Communications, vol. 21, no. 5, pp. 3083–3098, 2021.
  30. A. Goldsmith, S. A. Jafar, N. Jindal, and S. Vishwanath, “Capacity limits of MIMO channels,” IEEE J. Sel. Areas Commun., vol. 21, no. 5, pp. 684–702, 2003.
  31. E. Boshkovska, D. W. K. Ng, N. Zlatanov, and R. Schober, “Practical non-linear energy harvesting model and resource allocation for SWIPT systems,” IEEE Commun. Lett., vol. 19, no. 12, pp. 2082–2085, 2015.
  32. S. Zargari, A. Khalili, Q. Wu, M. R. Mili, and D. W. K. Ng, “Max-min fair energy-efficient beamforming design for intelligent reflecting surface-aided SWIPT systems with non-linear energy harvesting model,” IEEE Trans. Veh. Technol., vol. 70, no. 6, pp. 5848–5864, 2021.
  33. R. Long, Y.-C. Liang, Y. Pei, and E. G. Larsson, “Active reconfigurable intelligent surface-aided wireless communications,” IEEE Trans. Wirel. Commun., vol. 20, no. 8, pp. 4962–4975, 2021.
  34. A. Khalili, M. R. Mili, M. Rasti, S. Parsaeefard, and D. W. K. Ng, “Antenna selection strategy for energy efficiency maximization in uplink OFDMA networks: A multi-objective approach,” IEEE Trans. Wirel. Commun., vol. 19, no. 1, pp. 595–609, 2019.
  35. S. Diamond and S. Boyd, “CVXPY: A python-embedded modeling language for convex optimization,” J. Mach. Learn. Res., vol. 17, no. 1, pp. 2909–2913, 2016.
  36. Y. Yuan, G. Zheng, K.-K. Wong, and K. B. Letaief, “Meta-reinforcement learning based resource allocation for dynamic V2X communications,” IEEE Trans. Veh. Technol., vol. 70, no. 9, pp. 8964–8977, 2021.
  37. Y. Eghbali, S. Faramarzi, S. K. Taskou, M. R. Mili, M. Rasti, and E. Hossain, “Beamforming for STAR-RIS-aided integrated sensing and communication using meta DRL,” IEEE Wirel. Commun., 2024.
  38. Q. Wu, S. Zhang, B. Zheng, C. You, and R. Zhang, “Intelligent reflecting surface-aided wireless communications: A tutorial,” IEEE Trans. Commun., vol. 69, no. 5, pp. 3313–3351, 2021.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets