Cognitive Radio Networks for Wireless Communications using Spectrum Sensing Technique: A Review
Main Article Content
Abstract
The exponential growth of wireless communication technologies has led to an increasing demand for radio spectrum, resulting in inefficient utilization and severe spectrum scarcity issues. Cognitive Radio Networks (CRNs) have emerged as a promising solution to address this challenge by enabling dynamic spectrum access. CRNs allow unlicensed users, known as secondary users (SUs), to opportunistically utilize the underutilized spectrum allocated to licensed or primary users (PUs) without causing harmful interference. Among the core functions of CRNs, spectrum sensing plays a pivotal role in detecting vacant spectrum bands (spectrum holes) and ensuring seamless and interference-free communication.
This review provides a comprehensive analysis of various spectrum sensing techniques used in CRNs, including energy detection, matched filtering, cyclostationary feature detection, and cooperative sensing. Each technique is examined in terms of its operational principles, advantages, limitations, and applicability under different signal and channel conditions. The paper also discusses key performance metrics such as probability of detection, false alarm rate, and sensing time, which are critical for evaluating sensing accuracy. Furthermore, the review highlights recent advances such as machine learning-based sensing, compressive sensing, and their potential integration with emerging technologies like 5G, 6G, and IoT. The paper concludes by identifying key challenges and proposing future research directions aimed at improving the efficiency, reliability, and adaptability of spectrum sensing in cognitive radio networks.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
J. Yao et al., "FAS-Driven Spectrum Sensing for Cognitive Radio Networks," arXiv preprint, arXiv:2411.08383, Nov. 2024.
Y. Xu, Y. Li, and T. Q. S. Quek, "RIS-Enhanced Cognitive Integrated Sensing and Communication: Joint Beamforming and Spectrum Sensing," arXiv preprint, arXiv:2402.06879, Feb. 2024.
M. Wasilewska, H. Bogucka, and H. V. Poor, "Secure Federated Learning for Cognitive Radio Sensing," arXiv preprint, arXiv:2304.06519, Mar. 2023.
R. M. Alonso et al., "Multi-objective Optimization of Cognitive Radio Networks," arXiv preprint, arXiv:2405.02694, May 2024.
S. A. Khan, M. A. Khan, and M. A. Khan, "Deep Learning-CT Based Spectrum Sensing for Cognitive Radio for 6G Wireless Communication," ICT Express, vol. 10, no. 1, pp. 1–8, Jan. 2024.
Yiru Liu, Bo Ai and Jiayi Zhang, “Downlink Spectral Efficiency of Massive MIMO Systems with Mutual Coupling”, Special Issue MIMO System Technology for Wireless Communications, vol. 12, No. 6, 2023.
Shao, Z.; Yan, W.; Yuan, X. Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1-Bit ADCs and Oversampling. ZTE Commun. 20, 48–56, 2022.
A. Gharib, W. Ejaz and M. Ibnkahla, "Distributed spectrum sensing for IoT networks: Architecture challenges and learning", IEEE Internet of Things Magazine, vol. 4, no. 2, pp. 66-73, 2021.
J. Xie, J. Fang, C. Liu and X. Li, "Deep learning-based spectrum sensing in cognitive radio: A CNN-LSTM approach", IEEE Communications Letters, vol. 24, no. 10, pp. 2196-2200, 2020.
M. Zhang, J. Chen, S. He, L. Yang, X. Gong and J. Zhang, "Privacypreserving database assisted spectrum access for industrial Internet of Things: A distributed learning approach", IEEE Transactions on Industrial Electronics, vol. 67, no. 8, pp. 7094-7103, 2019.
Supraja Eduru and Nakkeeran Rangaswamy, “BER Analysis of Massive MIMO Systems under Correlated Rayleigh Fading Channel”, 9th ICCCNT IEEE 2018, IISC, Bengaluru, India.
H. Al-Hraishawi, G. Amarasuriya, and R. F. Schaefer, “Secure communication in underlay cognitive massive MIMO systems with pilot contamination,” in In Proc. IEEE Global Commun. Conf. (Globecom), pp. 1–7, Dec. 2017.
V. D. Nguyen et al., “Enhancing PHY security of cooperative cognitive radio multicast communications,” IEEE Trans. Cognitive Communication And Networking, vol. 3, no. 4, pp. 599–613, Dec. 2017.
R. Zhao, Y. Yuan, L. Fan, and Y. C. He, “Secrecy performance analysis of cognitive decode-and-forward relay networks in Nakagami-m fading channels,” IEEE Trans. Communication, vol. 65, no. 2, pp. 549–563, Feb. 2017.
W. Zhu, J. and. Xu and N. Wang, “Secure massive MIMO systems with limited RF chains,” IEEE Trans. Veh. Technol., vol. 66, no. 6, pp. 5455–5460, Jun. 2017.
W. Wang, K. C. Teh, and K. H. Li, “Enhanced physical layer security in D2D spectrum sharing networks,” IEEE Wireless Communication Letter, vol. 6, no. 1, pp. 106–109, Feb. 2017.
J. Zhang, G. Pan, and H. M. Wang, “On physical-layer security in underlay cognitive radio networks with full-duplex wireless-powered secondary system,” IEEE Access, vol. 4, pp. 3887–3893, Jul. 2016.
R. Zhang, X. Cheng, and L. Yang, “Cooperation via spectrum sharing for physical layer security in device-to-device communications under laying cellular networks,” IEEE Trans. Wireless Communication, vol. 15, no. 8, pp. 5651–5663, Aug. 2016.