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Hybrid Hierarchical DRL Enabled Resource Allocation for Secure Transmission in Multi-IRS-Assisted Sensing-Enhanced Spectrum Sharing Networks (2312.01071v1)

Published 2 Dec 2023 in cs.IT, eess.SP, and math.IT

Abstract: Secure communications are of paramount importance in spectrum sharing networks due to the allocation and sharing characteristics of spectrum resources. To further explore the potential of intelligent reflective surfaces (IRSs) in enhancing spectrum sharing and secure transmission performance, a multiple intelligent reflection surface (multi-IRS)-assisted sensing-enhanced wideband spectrum sharing network is investigated by considering physical layer security techniques. An intelligent resource allocation scheme based on double deep Q networks (D3QN) algorithm and soft Actor-Critic (SAC) algorithm is proposed to maximize the secure transmission rate of the secondary network by jointly optimizing IRS pairings, subchannel assignment, transmit beamforming of the secondary base station, reflection coefficients of IRSs and the sensing time. To tackle the sparse reward problem caused by a significant amount of reflection elements of multiple IRSs, the method of hierarchical reinforcement learning is exploited. An alternative optimization (AO)-based conventional mathematical scheme is introduced to verify the computational complexity advantage of our proposed intelligent scheme. Simulation results demonstrate the efficiency of our proposed intelligent scheme as well as the superiority of multi-IRS design in enhancing secrecy rate and spectrum utilization. It is shown that inappropriate deployment of IRSs can reduce the security performance with the presence of multiple eavesdroppers (Eves), and the arrangement of IRSs deserves further consideration.

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References (42)
  1. W. Wu, F. Yang, F. Zhou, Q. Wu, and R. Q. Hu, “Intelligent resource allocation for IRS-enhanced OFDM communication systems: A hybrid deep reinforcement learning approach,” IEEE Trans. Wireless Commun., vol. 22, no. 6, pp. 4028–4042, Jun. 2023.
  2. L. Wang, F. Yang, Y. Chen, S. Lai, and W. Wu, “Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM,” Phys. Commun., vol. 58, p. 102013, Jan. 2023.
  3. F. A. Aoudia and J. Hoydis, “End-to-end learning for OFDM: From neural receivers to pilotless communication,” IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 1049–1063, Feb. 2021.
  4. S. Kant, M. Bengtsson, B. Göransson, G. Fodor, and C. Fischione, “Efficient optimization for large-scale MIMO-OFDM spectral precoding,” IEEE Trans. Wireless Commun., vol. 20, no. 9, pp. 5496–5513, Sep. 2021.
  5. 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., vol. 22, no. 6, pp. 4028–4042, Sep. 2023.
  6. Y. Wu, F. Zhou, Q. Wu, Y. Huang, and R. Q. Hu, “Resource allocation for IRS-assisted sensing-enhanced wideband CR networks,” in Proc. IEEE Int. Conf. Commun. Work. (ICC Workshops), Jun. 2021, pp. 1–6.
  7. X. Zhu, Y. Huang, Q. Wu, F. Zhou, X. Ge, and Y. Liu, “Dynamic channel selection and transmission scheduling for cognitive radio networks,” IEEE Internet Things J., vol. 9, no. 23, pp. 24 429–24 443, Jun. 2022.
  8. D. H. Tashman and W. Hamouda, “An overview and future directions on physical-layer security for cognitive radio networks,” IEEE Netw., vol. 35, no. 3, pp. 205–211, May 2021.
  9. T. Sadig, M. Maleki, N. H. Tran, and H. R. Bahrami, “An encryption-aware PHY security framework for 4-node Gaussian wiretap channels with joint power constraint,” IEEE Trans. Commun., vol. 68, no. 12, pp. 7837–7850, Dec. 2020.
  10. X. Jiang, P. Li, Y. Zou, B. Li, and R. Wang, “Physical layer security for cognitive multiuser networks with hardware impairments and channel estimation errors,” IEEE Trans. Commun., vol. 70, no. 9, pp. 6164–6180, Sep. 2022.
  11. C. Gong, X. Yue, Z. Zhang, X. Wang, and X. Dai, “Enhancing physical layer security with artificial noise in large-scale NOMA networks,” IEEE Trans. Veh. Technol., vol. 70, no. 3, pp. 2349–2361, Mar. 2021.
  12. K. Lee, J. Bang, and H.-H. Choi, “Secrecy outage minimization for wireless-powered relay networks with destination-assisted cooperative jamming,” IEEE Internet Things J., vol. 8, no. 3, pp. 1467–1476, Feb. 2021.
  13. A. Pandey, S. Yadav, D.-T. Do, and R. Kharel, “Secrecy performance of cooperative cognitive AF relaying networks with direct links over mixed Rayleigh and double-Rayleigh fading channels,” IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 15 095–15 112, Dec. 2020.
  14. S. Hu, Z. Wei, Y. Cai, C. Liu, D. W. K. Ng, and J. Yuan, “Robust and secure sum-rate maximization for multiuser MISO downlink systems with self-sustainable IRS,” IEEE Trans. Commun., vol. 69, no. 10, pp. 7032–7049, Oct. 2021.
  15. B. Zheng, C. You, W. Mei, and R. Zhang, “A survey on channel estimation and practical passive beamforming design for intelligent reflecting surface aided wireless communications,” IEEE Commun. Surv. Tutor., vol. 24, no. 2, pp. 1035–1071, 2nd Quart. 2022.
  16. M. Cui, G. Zhang, and R. Zhang, “Secure wireless communication via intelligent reflecting surface,” IEEE Wireless Commun. Lett., vol. 8, no. 5, pp. 1410–1414, Oct. 2019.
  17. S. Gong, X. Lu, D. T. Hoang, D. Niyato, L. Shu, D. I. Kim, and Y.-C. Liang, “Toward smart wireless communications via intelligent reflecting surfaces: A contemporary survey,” IEEE Commun. Surv. Tutor., vol. 22, no. 4, pp. 2283–2314, 4th Quart. 2020.
  18. Q. Wu and R. Zhang, “Joint active and passive beamforming optimization for intelligent reflecting surface assisted SWIPT under QoS constraints,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1735–1748, Aug. 2020.
  19. A. Feriani and E. Hossain, “Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial,” IEEE Commun. Surv. Tutor., vol. 23, no. 2, pp. 1226–1252, 2nd Quart. 2021.
  20. N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y.-C. Liang, and D. I. Kim, “Applications of deep reinforcement learning in communications and networking: A survey,” IEEE Commun. Surv. Tutor., vol. 21, no. 4, pp. 3133–3174, 4th Quart. 2019.
  21. U. Challita and D. Sandberg, “Deep reinforcement learning for dynamic spectrum sharing of LTE and NR,” in Proc. IEEE Int. Conf. Commun. (ICC)., Jun. 2021, pp. 1–6.
  22. H. Zhang, N. Yang, W. Huangfu, K. Long, and V. C. M. Leung, “Power control based on deep reinforcement learning for spectrum sharing,” IEEE Trans. Wireless Commun., vol. 19, no. 6, pp. 4209–4219, Jun. 2020.
  23. W. Ejaz and M. Ibnkahla, “Multiband spectrum sensing and resource allocation for IoT in cognitive 5G networks,” IEEE Internet Things J., vol. 5, no. 1, pp. 150–163, Feb. 2018.
  24. F. Zhou, N. C. Beaulieu, J. Cheng, Z. Chu, and Y. Wang, “Robust max-min fairness resource allocation in sensing-based wideband cognitive radio with SWIPT: Imperfect channel sensing,” IEEE Sys. J., vol. 12, no. 3, pp. 2361–2372, Sep. 2018.
  25. D. Xu, X. Yu, Y. Sun, D. W. K. Ng, and R. Schober, “Resource allocation for IRS-assisted full-duplex cognitive radio systems,” IEEE Trans. Commun., vol. 68, no. 12, pp. 7376–7394, Dec. 2020.
  26. K. Feng, X. Li, Y. Han, S. Jin, and Y. Chen, “Physical layer security enhancement exploiting intelligent reflecting surface,” IEEE Commun. Lett., vol. 25, no. 3, pp. 734–738, Mar. 2021.
  27. S. Xu, J. Liu, and Y. Cao, “Intelligent reflecting surface empowered physical-layer security: Signal cancellation or jamming?” IEEE Internet Things J., vol. 9, no. 2, pp. 1265–1275, Jan. 2022.
  28. B. You, I.-H. Lee, and H. Jung, “Secrecy rate analysis of randomized radiation for intelligent reflecting surface-aided communication systems,” IEEE Commun. Lett., vol. 26, no. 9, pp. 1999–2003, Sep. 2022.
  29. H. Li, W. Cai, Y. Liu, M. Li, Q. Liu, and Q. Wu, “Intelligent reflecting surface enhanced wideband MIMO-OFDM communications: From practical model to reflection optimization,” IEEE Trans. Commun., vol. 69, no. 7, pp. 4807–4820, Jul. 2021.
  30. W. Jiang, B. Chen, J. Zhao, Z. Xiong, and Z. Ding, “Joint active and passive beamforming design for the IRS-assisted MIMOME-OFDM secure communications,” IEEE Trans. Veh. Technol., vol. 70, no. 10, pp. 10 369–10 381, Oct. 2021.
  31. X. Wu, J. Ma, Z. Xing, C. Gu, X. Xue, and X. Zeng, “Secure and energy efficient transmission for IRS-assisted cognitive radio networks,” IEEE Trans. Cogn. Commun. Netw., vol. 8, no. 1, pp. 170–185, Mar. 2022.
  32. J. Yuan, Y.-C. Liang, J. Joung, G. Feng, and E. G. Larsson, “Intelligent reflecting surface (IRS)-enhanced cognitive radio system,” in Proc. IEEE Int. Conf. Commun. (ICC)., Jun. 2020, pp. 1–6.
  33. A. Doshi, S. Yerramalli, L. Ferrari, T. Yoo, and J. G. Andrews, “A deep reinforcement learning framework for contention-based spectrum sharing,” IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2526–2540, Aug. 2021.
  34. X. Tan, L. Zhou, H. Wang, Y. Sun, H. Zhao, B.-C. Seet, J. Wei, and V. C. M. Leung, “Cooperative multi-agent reinforcement-learning-based distributed dynamic spectrum access in cognitive radio networks,” IEEE Internet Things J., vol. 9, no. 19, pp. 19 477–19 488, Oct. 2022.
  35. H. Yang, Z. Xiong, J. Zhao, D. Niyato, L. Xiao, and Q. Wu, “Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 375–388, Jan. 2021.
  36. C. Huang, G. Chen, and K.-K. Wong, “Multi-agent reinforcement learning-based buffer-aided relay selection in IRS-assisted secure cooperative networks,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 4101–4112, Aug. 2021.
  37. J. Kim, S. Hosseinalipour, T. Kim, D. J. Love, and C. G. Brinton, “Multi-IRS-assisted multi-cell uplink MIMO communications under imperfect CSI: A deep reinforcement learning approach,” in Proc. IEEE Int. Conf. Commun. Work. (ICC Workshops), Jun. 2021, pp. 1–7.
  38. S. Choi, H. Park, and T. Hwang, “Optimal beamforming and power allocation for sensing-based spectrum sharing in cognitive radio networks,” IEEE Trans. Veh. Technol., vol. 63, no. 1, pp. 412–417, Jan. 2014.
  39. Y. Li and A. H. Aghvami, “Intelligent uav navigation: A DRL-QiER solution,” in Proc. IEEE Int. Conf. Commun. (ICC)., 2022, pp. 419–424.
  40. Y. Fu, D. Wu, and B. Boulet, “A closer look at offline RL agents,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), vol. 35, 2022, pp. 8591–8604.
  41. J. Lyu, X. Ma, J. Yan, and X. Li, “Efficient continuous control with double actors and regularized critics,” in Proc. AAAI Conf. Artif. Intell. (AAAI)., vol. 36, no. 7, 2022, pp. 7655–7663.
  42. M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.1,” Mar. 2017, [Online]. Available: http://cvxr.com/cvx/.
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