RIS-GLRT for Robust Spectrum Sensing in MIMO Cognitive Radio Networks
Adeyeye Emmanuel A, Mbachu CB, Muoghalu CN
Abstract
Efficient utilization of the radio spectrum is essential for supporting the rapid growth of wireless communication services. Cognitive Radio (CR) offers a promising solution by enabling secondary users (SUs) to opportunistically access spectrum when primary users (PUs) are inactive. Reliable spectrum sensing remains a critical challenge, particularly in low signal-to-noise ratio (SNR) and fading environments, where conventional detectors such as energy detection and eigenvalue-based methods often fail. This paper proposes a Reconfigurable Intelligent Surface (RIS)-empowered Generalized Likelihood Ratio Test (GLRT) framework for robust spectrum sensing in Multiple-Input Multiple-Output (MIMO) CR systems. The RIS introduces controllable reflections that enhance the effective channel gain, thereby improving separability between signal-present and noise-only hypotheses. Analytical formulations for the GLRT test statistic, threshold estimation under a fixed false-alarm constraint, and Monte Carlo calibration are developed. Simulation results demonstrate that the proposed RIS-GLRT achieves up to 35β40% improvement in detection probability at moderate-to-low SNR compared to traditional detectors, while reducing missed detection probability and exhibiting favourable scaling with the number of antennas and RIS elements. These findings confirm the potential of RIS-assisted GLRT to significantly enhance spectrum sensing reliability and establish a strong foundation for next-generation spectrum-sharing systems.
Keywords
Cognitive Radio (CR)
Spectrum Sensing
Reconfigurable Intelligent Surface (RIS)
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the
University
of
Sydney,
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Intelligent Reflecting Surfaces. http://arxiv.org/abs/1904.09573
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detector based on mimo cognitive radios with non-perfect channel gain.
Electronics
(Switzerland),
10(5),
1β19.
https://doi.org/10.3390/electronics10050529
Boyd, S. P. ., & Vandenberghe, Lieven. (2014). Convex optimization. Cambridge
University Press.
Buzzi, S., Grossi, E., Lops, M., & Venturino, L. (2021). Foundations of MIMO Radar
Detection
Aided
by
Reconfigurable
Intelligent
Surfaces.
ArXiv.
https://doi.org/10.1109/TSP.2022.3157975
Cabrera-Tobar, A., Massi Pavan, A., Petrone, G., & Spagnuolo, G. (2022). A Review of
the Optimization and Control Techniques in the Presence of Uncertainties for the
Energy Management of Microgrids. MDPI, Basel, Switzerland, 15(23).
https://doi.org/10.3390/en15239114
Dimitri, B. (2016). Nonlinear Programming. Athena Scientific.
Elleuch, I., Abdelkefi, F., & Siala, M. (2013). Complexity Issues within Eigenvalue-Based
Multi-Antenna
Spectrum
Sensing.
Https://Services.Igi-
Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/978-1-4666-6571-2.Ch023,
2, 603β617. https://doi.org/10.4018/978-1-4666-6571-2.CH023
Font-Segura, J., & Wang, X. (2010). GLRT-based spectrum sensing for cognitive radio
with prior information. IEEE Transactions on Communications, 58(7), 2137β
https://doi.org/10.1109/TCOMM.2010.07.090556
Ge, J., Liang, Y. C., Li, S., & Bai, Z. (2022). RIS-Enhanced Spectrum Sensing: How
Many Reflecting Elements are Required to Achieve a Detection Probability Close
to 1? IEEE Transactions on Wireless Communications, 21(10), 8600β8615.
https://doi.org/10.1109/TWC.2022.3167474
Gudla, V. V., Kumaravelu, V. B., Imoize, A. L., Castillo Soria, F. R., Sujatha, A. B., John
Kennedy, H. S., Jadhav, H. K., Murugadass, A., & Sur, S. N. (2025). Performance
Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave
Massive MIMO System Under 3GPP 5G Channels. Information (Switzerland),
16(5). https://doi.org/10.3390/info16050396
Han, Y., Tang, W., Jin, S., Wen, C. K., & Ma, X. (2019). Large intelligent surface-
Assisted wireless communication exploiting statistical CSI. IEEE Transactions on
Vehicular
Technology,
68(8),
8238β8242.
https://doi.org/10.1109/TVT.2019.2923997
Hong, I. P. (2023). Reviews Based on the Reconfigurable Intelligent Surface Technical
Issues. In Electronics (Switzerland) (Vol. 12, Issue 21). Multidisciplinary Digital
Publishing Institute (MDPI). https://doi.org/10.3390/electronics12214489
Hu, X., Yi, Y., Li, K., Zhang, H., & Kai, C. (2022). Active Reconfigurable Intelligent
Surface Aided Surveillance Scheme. IEEE Wireless Communications Letters,
12(2). https://doi.org/10.1109/LWC.2022.3227028
Kayraklik, S., Yildirim, I., Basar, E., Hokelek, I., & Gorcin, A. (2024). Practical
Implementation of RIS-Aided Spectrum Sensing: A Deep Learning-Based
Solution.
IEEE
Systems
Journal,
18(2),
1481β1488.
https://doi.org/10.1109/JSYST.2024.3376986
Khandelwal, A., & Charan, C. (2017). Simulation analysis of MP and eigenvalue based
method for cognitive radio. 2016 International Conference on Emerging Trends
in Communication Technologies, ETCT 2016, November 2016, 1β5.
https://doi.org/10.1109/ETCT.2016.7882959
Kim, Y. J. (2017). Monte Carlo vs. fuzzy Monte Carlo simulation for uncertainty and
global
sensitivity
analysis.
Sustainability
(Switzerland),
9(4).
https://doi.org/10.3390/su9040539
Lee, Y. (2022). Multiobjective Optimization for Intelligent Reflective Surface-Aided
Physical-Layer Multicasting. IEEE Open Journal of the Communications Society,
3, 411β423. https://doi.org/10.1109/OJCOMS.2022.3156163
Liang, Y. C., Chen, K. C., Li, G. Y., & MΓ€hΓΆnen, P. (2011). Cognitive radio networking
and communications: An overview. IEEE Transactions on Vehicular Technology,
60(7), 3386β3407. https://doi.org/10.1109/TVT.2011.2158673
Liang, Y. C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff
for cognitive radio networks. IEEE Transactions on Wireless Communications,
7(4), 1326β1337. https://doi.org/10.1109/TWC.2008.060869
Mehrabian, A., & Zaimbashi, A. (2018). GLRT-Based Spectrum Sensing for SIMO
Cognitive Radio with Transmitter IQI. 26th Iranian Conference on Electrical
Engineering, ICEE 2018, 378β382. https://doi.org/10.1109/ICEE.2018.8472656
Muduli, A., & Panwar, K. (2021). A reconfigurable Filtenna for Cognitive Radio
Application.
Journal
of
Physics:
Conference
Series,
1817(1).
https://doi.org/10.1088/1742-6596/1817/1/012002
Pandya, P., Durvesh, A., & Parekh, N. (2015). Energy detection based spectrum sensing
for cognitive radio network. Proceedings - 2015 5th International Conference on
Communication Systems and Network Technologies, CSNT 2015, 201β206.
https://doi.org/10.1109/CSNT.2015.264
Parihar, N., Mankar, P. D., & Chaudhari, S. (2024). Maximum Eigenvalue Detection
based Spectrum Sensing in RIS-aided System with Correlated Fading.
http://arxiv.org/abs/2311.08296
Patel, A., Biswas, S., & Jagannatham, A. K. (2016). Optimal GLRT-Based Robust
Spectrum Sensing for MIMO Cognitive Radio Networks With CSI Uncertainty.
IEEE
Transactions
on
Signal
Processing,
64(6),
1621β1633.
https://doi.org/10.1109/TSP.2015.2500183
Song, C., & Kawai, R. (2023). Monte Carlo and variance reduction methods for structural
reliability analysis: A comprehensive review. School of Mathematics and Statistics
at
the
University
of
Sydney,
3(15).
https://doi.org/10.13140/RG.2.2.25762.50883/1
Song, X., Zou, L., & Tang, M. (2025). An Improved Monte Carlo Reliability Analysis
Method Based on BP Neural Network. Applied Sciences (Switzerland), 15(8).
https://doi.org/10.3390/app15084438
Wei, B., Zhang, P., & Zhang, Q. (2024). Active Reconfigurable Intelligent Surface-Aided
Over-the-Air Computation Networks. IEEE Wireless Communications Letters,
13(4), 1148β1152. https://doi.org/10.1109/LWC.2024.3363214
Wu, Q., & Zhang, R. (2019). Intelligent Reflecting Surface Enhanced Wireless Network
via Joint Active and Passive Beamforming. IEEE Transactions on Wireless
Communications,
18(11),
5394β5409.
https://doi.org/10.1109/TWC.2019.2936025
Yu, X., Xu, D., & Schober, R. (2020). Enabling Secure Wireless Communications via
Intelligent Reflecting Surfaces. http://arxiv.org/abs/1904.09573
