DOI : https://doi.org/10.18517/ijaseit.15.1.20491

Analysis of Factors Affecting Spectrum Sensing in an IRS based Wireless Network

Mahmoud Zaki Iskandarani (1), Rahmat Hidayat (2)
(1) Faculty of Engineering, Department of Robotics and Artificial Intelligence Engineering Al-Ahliyya Amman University, Amman, Jordan
(2) Department of Information Technology, Politeknik Negeri Padang, Padang, Indonesia
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M. Z. Iskandarani and R. Hidayat, “Analysis of Factors Affecting Spectrum Sensing in an IRS based Wireless Network”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 300–309, Feb. 2025.
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Intelligent reflecting surface, or IRS, has shown great promise for wireless networks. IRS provides flexible wireless channel control and setup by dynamically adjusting the reflection amplitudes/phase shifts of numerous devices. This greatly increases the wireless signal transmission rate and dependability. In cognitive radio networks, spectrum sensing and communication security are critical components. Intelligent reflecting surfaces (IRS) to improve the sensing performance and the accuracy of spectrum sensing at the same time. This work and through MATLAB simulation, carry out analysis of the effect of user traffic (Tuser), noise factor (Nfactor), and probability of false alarm (Pfalse) on the ability of an IRS based wireless system to spectrum sense through computation of detection probability (Pdetection). The work provided results, analysis, and mathematical model for both stable environmental conditions, and unstable ones. In addition, two Signal-to-Noise Ratio (SNR) levels are taken into account, 20% and 80% noise. This enables assessing of spectrum sensing under different conditions. The results shows that Pdetection decreases as Nfactor increases per percentage of Tuser with a logarithmic shape function. The work also uncovers that there is a tenfold increase in the noise factor as the user traffic level goes above 50%. As the noise factor increases for a fixed probability of false alarm, and with user traffic increase, detection probability decreases, and as Pfalse increases, so does the Pdetection. The obtained results indicate that there should be a balance in Tuser with Nfactor in order to optimize Pdetection and reduce the effect of Pfalse in spectrum sensing

Z. Chu, Z. Zhu, F. Zhou, M. Zhang, and N. Al-Dhahir, “Intelligent reflecting surface assisted wireless powered sensor networks for Internet of Things,” IEEE Trans. Commun., vol. 69, no. 7, pp. 4877–4889, Jul. 2021, doi: 10.1109/tcomm.2021.3074539.

W. Mei, B. Zheng, C. You, and R. Zhang, “Intelligent reflecting surface-aided wireless networks: From single-reflection to multireflection design and optimization,” Proc. IEEE, vol. 110, no. 9, pp. 1380–1400, Sep. 2022, doi: 10.1109/jproc.2022.3170656.

D. Pérez-Adán, Ó. Fresnedo, J. P. Gonzalez-Coma, and L. Castedo, “Intelligent reflective surfaces for wireless networks: An overview of applications, approached issues, and open problems,” Electronics, vol. 10, no. 19, p. 2345, Oct. 2021, doi: 10.3390/electronics10192345.

Y. Wang, B. Ji, and D. Li, “IRS assist wireless communication: Scenarios, advantages, convergence,” J. Comput. Electron. Inf. Manag., vol. 10, no. 3, pp. 40–45, 2023, doi: 10.54097/jceim.v10i3.8679.

O. Bouhamed, H. Ghazzai, H. Besbes, and Y. Massoud, “A UAV-assisted data collection for wireless sensor networks: Autonomous navigation and scheduling,” IEEE Access, vol. 8, pp. 110446–110460, 2020, doi: 10.1109/access.2020.3002538.

Z. Zhang, W. Chen, Q. Wu, Z. Li, X. Zhu, and J. Chen, “Multiple intelligent reflecting surfaces collaborative wireless localization system,” IEEE Trans. Wireless Commun., vol. 24, no. 1, pp. 134–148, Jan. 2025, doi: 10.1109/twc.2024.3488822.

K. Aswini and M. Surendar, “Performance analyses of intelligent reflecting surface aided downlink multi-user rate-splitting multiple access system for 6G applications,” Comput. Netw., vol. 242, p. 110271, 2024, doi: 10.1016/j.comnet.2024.110271.

V. Srivastava and B. Prasad, “IRS assisted spectrum sensing in cognitive radio network with grey wolf optimization,” Phys. Commun., vol. 66, p. 102436, 2024, doi: 10.1016/j.phycom.2024.102436.

L. Al-Zabin, O. Al-Wesabi, H. Al Hajri, N. Abdullah, B. Khudayer, and H. Al Lawati, “Probabilistic detection of indoor events using a wireless sensor network-based mechanism,” Sensors, vol. 23, no. 15, p. 6198, 2023, doi: 10.3390/s23156198.

S. Sur and R. Bera, “Intelligent reflecting surface assisted MIMO communication system: A review,” Phys. Commun., vol. 47, p. 101386, 2021, doi: 10.1016/j.phycom.2021.101386.

W. Tan, Q. Zhou, W. Tan, L. Yang, and C. Li, “Performance analysis of intelligent reflecting surface assisted wireless communication system,” Comput. Model. Eng. Sci., vol. 137, no. 1, pp. 775–787, 2023, doi: 10.32604/cmes.2023.027427.

Z. Wang, W. Wu, F. Zhou, B. Wang, Q. Wu, T. Quek, and C. Byoung, “IRS-enhanced spectrum sensing and secure transmission in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 10271–10286, Aug. 2024, doi: 10.1109/twc.2024.3370812.

B. Zheng, X. Xiong, T. Ma, J. Tang, D. Ng, and A. Swindlehurst, “Intelligent reflecting surface-enabled anti-detection for secure sensing and communications,” IEEE Wireless Commun. (Early Access), 2025, doi: 10.1109/mwc.006.2400129.

W. Wu, Z. Wang, L. Yuan, F. Zhou, F. Lang, and B. Wang, “IRS-enhanced energy detection for spectrum sensing in cognitive radio,” IEEE Wireless Commun. Lett., vol. 10, no. 10, pp. 2254–2258, Oct. 2021, doi: 10.1109/lwc.2021.3099121.

K. Aswini and M. Surendar, “Capacity analysis of intelligent reflecting surface assisted RSMA system with perfect and imperfect CSI for 6G,” J. Phys.: Conf. Ser., vol. 2466, no. 1, p. 012001, 2023, doi: 10.1088/1742-6596/2466/1/012001.

G. Peng and W. Wu, “Fusion schemes based on IRS-enhanced cooperative spectrum sensing for cognitive radio networks,” Electronics, vol. 11, no. 16, p. 2533, 2022, doi: 10.3390/electronics11162533.

R. Kumar and S. Singh, “Intelligent reflecting surface framework for ED based spectrum sensing,” Int. J. Wirel. Inf. Networks, vol. 31, no. 2, pp. 155–162, 2024, doi: 10.1007/s10776-024-00619-z.

I. Yildirim, F. Kilinc, E. Basar, and G. Alexandropoulos, “Hybrid RIS-empowered reflection and decode-and-forward relaying for coverage extension,” IEEE Commun. Lett., vol. 25, no. 5, pp. 1692–1696, May 2021, doi: 10.1109/lcomm.2021.3054819.

R. Alhamad, “Spectrum sensing using intelligent reflecting surfaces with multi-antennas energy harvesting,” Wirel. Pers. Commun., vol. 135, pp. 1103–1116, 2024, doi: 10.1007/s11277-024-11110-6.

R. Alhamad, “Cognitive radio networks using intelligent reflecting surfaces,” Comput. Syst. Sci. Eng., vol. 43, no. 2, pp. 751–765, 2022, doi: 10.32604/csse.2022.021932.

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, doi: 10.1109/lwc.2022.3149834.

M. Saber et al., “Reconfigurable intelligent surfaces improved spectrum sensing in cognitive radio networks,” Procedia Comput. Sci., vol. 207, pp. 4113–4122, 2022, doi: 10.1016/j.procs.2022.09.474.

H. Tran and B. Lee, “Enhancing reconfigurable intelligent surface-enabled cognitive analysis and parameter optimization,” Sensors, vol. 24, no. 15, p. 4869, 2024, doi: 10.3390/s24154869.

R. Kumar, S. Singh, S. Chauhan, A. Anand, and A. Kumar, “ED based spectrum sensing over IRS-assisted Rayleigh-FTR fading channels,” AEU - Int. J. Electron. Commun., vol. 171, p. 154908, 2023, doi: 10.1016/j.aeue.2023.154908.

R. Alhamad and H. Boujemaa, “Intelligent reflecting surfaces with adaptive transmit power for underlay cognitive radio networks,” Wirel. Commun. Mobile Comput., vol. 2022, p. 2787466, 2022, doi: 10.1155/2022/2787466.

A. Elbir, K. Mishra, M. Shankar, and S. Chatzinotas, “The rise of intelligent reflecting surfaces in integrated sensing and communications paradigms,” IEEE Netw., vol. 37, no. 6, pp. 223–241, Nov. 2023, doi: 10.1109/mnet.128.2200446.

J. Lorincz, I. Ramljak, and D. Begušić, “A review of the noise uncertainty impact on energy detection with different OFDM system designs,” Comput. Commun., vol. 148, pp. 185–208, 2019, doi: 10.1016/j.comcom.2019.09.013.

K. Felizardo and E. Arboleda, “Next-generation antennas key enabler: Intelligent reflecting surfaces (IRS) technology potential to address limitations of traditional antennas,” Int. J. Sci. Res. Archive, vol. 12, no. 1, pp. 2608–2613, 2024, doi: 10.30574/ijsra.2024.12.1.1136.

X. Shao, C. You, and R. Zhang, “Intelligent reflecting surface aided wireless sensing: Applications and design issues,” IEEE Wireless Commun., vol. 31, no. 3, pp. 383–389, Jun. 2024, doi: 10.1109/mwc.004.2300058.

J. An, H. Li, D. Ng, and C. Yuen, “Fundamental detection probability vs. achievable rate tradeoff in integrated sensing and communication systems,” IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 9835–9853, Dec. 2023, doi: 10.1109/twc.2023.3273850.

F. Okogbaa, Q. Ahmed, F. Khan, W. Abbas, S. Zaidi, and T. Alade, “Design and application of intelligent reflecting surface (IRS) for beyond 5G wireless networks: A review,” Sensors, vol. 22, no. 7, p. 2436, 2022, doi: 10.3390/s22072436.

Y. Sun, J. Huang, and F. Wei, “Performance evaluation of distributed multi-agent IoT monitoring based on intelligent reflecting surface,” EURASIP J. Adv. Signal Process., vol. 2024, no. 4, 2024, doi: 10.1186/s13634-024-01132-4.

S. Nandan and M. Rahiman, “Intelligent reflecting surface (IRS) assisted mmWave wireless communication systems: A survey,” J. Commun., vol. 17, no. 9, pp. 745–760, 2022, doi: 10.12720/jcm.17.9.745-760.

A. Singh, A. Maurya, R. Prakash, P. Thakur, and B. Tiwari, “Reconfigurable intelligent surface with 6G for industrial revolution: Potential applications and research challenges,” Paladyn, J. Behav. Robot., vol. 14, no. 1, p. 20220114, 2023, doi: 10.1515/pjbr-2022-0114.

M. Sejan, M. Rahman, B. Shin, J. Oh, Y. You, and H. Song, “Machine learning for intelligent-reflecting-surface-based wireless communication towards 6G: A review,” Sensors, vol. 22, no. 14, p. 5405, 2022, doi: 10.3390/s22145405.

D. Wang, J. Zhang, and Q. Zhang, “Intelligent reflecting surface-assisted secrecy wireless communication with imperfect CSI,” Phys. Commun., vol. 44, p. 101235, 2021, doi: 10.1016/j.phycom.2020.101235.

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