Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
133 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 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

Active Reconfigurable Intelligent Surface Enhanced Spectrum Sensing for Cognitive Radio Networks (2311.16568v2)

Published 28 Nov 2023 in cs.IT, eess.SP, and math.IT

Abstract: In opportunistic cognitive radio networks, when the primary signal is very weak compared to the background noise, the secondary user requires long sensing time to achieve a reliable spectrum sensing performance, leading to little remaining time for the secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS) assisted spectrum sensing system, where the received signal strength from the interested primary user can be enhanced and underlying interference within the background noise can be mitigated as well. In comparison with the passive RIS, the active RIS can not only adapt the phase shift of each reflecting element but also amplify the incident signals. Notably, we study the reflecting coefficient matrix (RCM) optimization problem to improve the detection probability given a maximum tolerable false alarm probability and limited sensing time. Then, we show that the formulated problem can be equivalently transformed to a weighted mean square error minimization problem using the principle of the well-known weighted minimum mean square error (WMMSE) algorithm, and an iterative optimization approach is proposed to obtain the optimal RCM. In addition, to fairly compare passive RIS and active RIS, we study the required power budget of the RIS to achieve a target detection probability under a special case where the direct links are neglected and the RIS-related channels are line-of-sight. Via extensive simulations, the effectiveness of the WMMSE-based RCM optimization approach is demonstrated. Furthermore, the results reveal that the active RIS can outperform the passive RIS when the underlying interference within the background noise is relatively weak, whereas the passive RIS performs better in strong interference scenarios because the same power budget can support a vast number of passive reflecting elements for interference mitigation.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. J. Ge, Y.-C. Liang, and S. Sun, “Active RIS enhanced spectrum sensing for opportunistic cognitive radio networks,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Kuala Lumpur, Malaysia, 2023, pp. 3252–3257.
  2. X. You et al., “Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts,” Sci. China Inf. Sci., vol. 64, no. 1, pp. 1–74, Nov. 2021.
  3. Y. Zeng, Y.-C. Liang, A. T. Hoang, and R. Zhang, “A review on spectrum sensing for cognitive radio: challenges and solutions,” EURASIP J. Adv. Signal Process., vol. 2010, pp. 1–15, Jan. 2010.
  4. Y. Zeng and Y.-C. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” IEEE Trans. Commun., vol. 57, no. 6, pp. 1784–1793, Jun. 2009.
  5. A. Ghasemi and E. S. Sousa, “Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs,” IEEE Commun. Mag., vol. 46, no. 4, pp. 32–39, Apr. 2008.
  6. Y. Zeng, Y.-C. Liang, A. T. Hoang, and E. C. Peh, “Reliability of spectrum sensing under noise and interference uncertainty,” in Proc. IEEE Int. Conf. Commun. Workshops, Dresden, Germany, 2009, pp. 1–5.
  7. M. Lin, W. Wang, X. Hong, and W. Zhang, “GLRT approach for multi-antenna-based spectrum sensing under interference,” IEEE Commun. Lett., vol. 24, no. 7, pp. 1524–1528, Jul. 2020.
  8. C. Chen, H. Cheng, and Y.-D. Yao, “Cooperative spectrum sensing in cognitive radio networks in the presence of the primary user emulation attack,” IEEE Trans. Wireless Commun., vol. 10, no. 7, pp. 2135–2141, Jul. 2011.
  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, Jan. 2019.
  10. E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M.-S. Alouini, and R. Zhang, “Wireless communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116 753–116 773, 2019.
  11. Y.-C. Liang, R. Long, Q. Zhang, J. Chen, H. V. Cheng, and H. Guo, “Large intelligent surface/antennas (LISA): Making reflective radios smart,” J. Commun. Inf. Netw., vol. 4, no. 2, pp. 40–50, Jun. 2019.
  12. Y. Liu, X. Liu, X. Mu, T. Hou, J. Xu, M. Di Renzo, and N. Al-Dhahir, “Reconfigurable intelligent surfaces: Principles and opportunities,” IEEE Commun. Surveys Tuts., vol. 23, no. 3, pp. 1546–1577, 3rd quarter 2021.
  13. H. Zhou, Q. Zhang, Y.-C. Liang, and Y. Pei, “Assistance-transmission tradeoff for RIS-assisted symbiotic radios,” IEEE Trans. Wireless Commun., 2023.
  14. Q. Zhang, H. Zhou, Y.-C. Liang, W. Zhang, and H. V. Poor, “Channel capacity of RIS-assisted symbiotic radios with imperfect knowledge of channels,” IEEE Trans. Cogn. Commun. Netw., 2024.
  15. H. Chen, R. Long, and Y.-C. Liang, “Transmission protocol and beamforming design for ris-assisted symbiotic radio over ofdm carriers,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Kuala Lumpur, Malaysia, 2023, pp. 3258–3263.
  16. J. Chen, Y.-C. Liang, Y. Pei, and H. Guo, “Intelligent reflecting surface: A programmable wireless environment for physical layer security,” IEEE Access, vol. 7, pp. 82 599–82 612, 2019.
  17. T. Jiang and W. Yu, “Interference nulling using reconfigurable intelligent surface,” IEEE J. Sel. Areas Commun., vol. 40, no. 5, pp. 1392–1406, May 2022.
  18. Y. Liu, X. Mu, X. Liu, M. Di Renzo, Z. Ding, and R. Schober, “Reconfigurable intelligent surface-aided multi-user networks: Interplay between NOMA and RIS,” IEEE Wireless Commun., vol. 29, no. 2, pp. 169–176, Apr. 2022.
  19. C. Pan, H. Ren, K. Wang, W. Xu, M. Elkashlan, A. Nallanathan, and L. Hanzo, “Multicell MIMO communications relying on intelligent reflecting surfaces,” IEEE Trans. Wireless Commun., vol. 19, no. 8, pp. 5218–5233, Aug. 2020.
  20. J. Ge, Y.-C. Liang, S. Li, and Z. Bai, “RIS-enhanced spectrum sensing: How many reflecting elements are required to achieve a detection probability close to 1?” IEEE Trans. Wireless Commun., no. 10, pp. 8600–8615, Oct. 2022.
  21. 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 Wireless Commun. Lett., vol. 10, no. 10, pp. 2254–2258, Oct. 2021.
  22. S. Lin, B. Zheng, F. Chen, and R. Zhang, “Intelligent reflecting surface-aided spectrum sensing for cognitive radio,” IEEE Wireless Commun. Lett., vol. 11, no. 5, pp. 928–932, May 2022.
  23. A. Nasser, H. A. H. Hassan, A. Mansour, K.-C. Yao, and L. Nuaymi, “Intelligent reflecting surfaces and spectrum sensing for cognitive radio networks,” IEEE Trans. Cogn. Commun. Netw., vol. 8, no. 3, pp. 1497–1511, Sep. 2022.
  24. W. Wu, Z. Wang, Y. Wu, F. Zhou, B. Wang, Q. Wu, and D. W. K. Ng, “Joint sensing and transmission optimization for IRS-assisted cognitive radio networks,” IEEE Trans. Wireless Commun., Early Access, 2023.
  25. J. Ge, S. Wang, C. Sun, and Y.-C. Liang, “RIS-enhanced cooperative spectrum sensing for opportunistic cognitive radio networks,” in 2023 IEEE Globecom Workshops (GC Wkshps), Kuala Lumpur, Malaysia, 2023, pp. 1427–1432.
  26. R. Long, Y.-C. Liang, Y. Pei, and E. G. Larsson, “Active reconfigurable intelligent surface-aided wireless communications,” IEEE Trans. Wireless Commun., vol. 20, no. 8, pp. 4962–4975, Aug. 2021.
  27. Y. Chen, J. Wang, and Y.-C. Liang, “Active reconfigurable intelligent surface-assisted bistatic backscatter communications,” in 2023 IEEE Globecom Workshops (GC Wkshps), Kuala Lumpur, Malaysia, 2023, pp. 1716–1721.
  28. X. Li, Q. Zhu, T. Yu, and Y. Chen, “Active RIS assisted spectrum sharing: Able to achieve energy-efficient notable detection performance gains,” IEEE Trans. Veh. Technol., vol. 72, no. 9, pp. 11 668–11 684, Sep. 2023.
  29. H. Xie, B. Gu, and D. Li, “Enhancing spectrum sensing via reconfigurable intelligent surfaces: Passive or active sensing and how many reflecting elements are needed?” arXiv preprint arXiv:2306.13874, 2023.
  30. Y. Zeng, C. L. Koh, and Y.-C. Liang, “Maximum eigenvalue detection: Theory and application,” in Proc. IEEE Int. Conf. Commun. (ICC), Beijing, China, 2008, pp. 4160–4164.
  31. J. Ge, Y.-C. Liang, Z. Bai, and G. Pan, “Large-dimensional random matrix theory and its applications in deep learning and wireless communications,” Random Matrices, Theory Appl., vol. 10, no. 04, p. 2230001, 2021.
  32. X. Zhao, S. Lu, Q. Shi, and Z.-Q. Luo, “Rethinking WMMSE: Can its complexity scale linearly with the number of BS antennas?” IEEE Trans. Signal Process., vol. 71, pp. 433–446, 2023.
  33. Q. Shi, W. Xu, J. Wu, E. Song, and Y. Wang, “Secure beamforming for MIMO broadcasting with wireless information and power transfer,” IEEE Trans. Wireless Commun., vol. 14, no. 5, pp. 2841–2853, May 2015.
  34. M. Grant and S. Boyd, “CVX: MATLAB software for disciplined convex programming, version 2.2,” http://cvxr.com/cvx, Jan. 2020.
  35. X. Jia, X. Zhou, D. Niyato, and J. Zhao, “Intelligent reflecting surface-assisted bistatic backscatter networks: Joint beamforming and reflection design,” IEEE Trans. Green Commun. Netw., vol. 6, no. 2, pp. 799–814, Jun. 2021.
  36. C. R. Stevenson, G. Chouinard, Z. Lei, W. Hu, S. J. Shellhammer, and W. Caldwell, “IEEE 802.22: The first cognitive radio wireless regional area network standard,” IEEE Commun. Mag., vol. 47, no. 1, pp. 130–138, Jan. 2009.

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