SDR-based Intelligent Cooperative Spectrum Sensing for Cognitive Radio Systems
Engineering and Technology Journal,
2023, Volume 41, Issue 2, Pages 1-11
AbstractIn this paper, the software and hardware of a software-defined radio (SDR) platform are used to implement and verify the blind real-time sensing act of intelligent collaborative spectrum sensing based on a new theoretical formula for constructing denoised mixed features named MSKU3 and paired with an unsupervised machine learning K-Medoids algorithm. Two low-cost RTL-SDR dongle hardware receivers are used as two cooperative unlicensed secondary users to capture the radio frequency of a licensed primary user channel. A host personal computer is used as a fusion center to run GNU-Radio software signal processing blocks to implement the developed method, and a single Universal Software Radio Peripheral (USRP) N210 hardware transmitter based on FPGA is used to take up unoccupied desired radio frequency bandwidth. Two scenarios of signal-to-noise ratio levels have been adopted to verify and test the sensing performance of the developed system. The first one occurs when unlicensed secondary users have equal signal-to-noise ratio values. The second occurs when unlicensed secondary users have different signal-to-noise ratio values since each secondary user has their location. The experimental results of detecting action in terms of the probability of detection for the proposed method show that the theoretical and practical results are very close to each other.
- The real-time sensing performance of the new intelligent cooperative spectrum sensing based on the denoised mixed feature method paired with K-Medoids is verified.
- Two RTL-SDR hardware receivers, a host laptop computer, and a USRP-SDR hardware transmitter are used.
- The theoretical and practical sensing performance validity of the developed scheme is confirmed based on the SDR platform.
 H. S. Abed, H. N. Abdullah, and M. A. Mahmood, Real-Time Hardware Implementation of Cyclostationary Spectrum Sensing for Various Modulation Types Using USRP, Int. Conf. Sp. Sci. Commun. Iconsp., 2021,2021, 54–59. doi: 10.1109/IconSpace53224.2021.9768689.
 C. Charan and R. Pandey, Cooperative spectrum sensing using eigenvalue-based double-threshold detection scheme for cognitive radio networks, Adv. Intell. Syst. Comput., 697 (2019) 189–199.doi: 10.1007/978-981-13-1822-1_18.
 R. Ouyang, T. Matsumura, K. Mizutani, and H. Harada, Software-Defined Radio-Based Evaluation Platform for Highly Mobile IEEE 802.22 System, IEEE Open J. Veh. Technol., 3 (2022) 167–177. doi:10.1109/OJVT.2022.3164461.
 X. Tan, L. Zhou, H. Wang, Y. Sun, H. Zhao, and B. C. Seet, Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks, IEEE Int. Thi. J., 2022. doi:10.1109/JIOT.2022.3168296.
 V. Ramani and S. K. Sharma, Cognitive radios: A survey on spectrum sensing, security and spectrum handoff, China Commun., 14 (2017) 185–208. doi: 10.1109/CC.2017.8233660.
 A. Ali and W. Hamouda, Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications, IEEE Commun. Surv. Tuts., 19 (2017) 1277–1304. doi: 10.1109/COMST.2016.2631080.
 H. Oh and H. Nam, Energy detection scheme in the presence of burst signals, IEEE Signal Process. Lett., vol. 26 (2019) 582–586. doi: 10.1109/LSP.2019.2900165.
 R. Shrestha and S. S. Telgote, A short sensing-time cyclostationary feature detection based spectrum sensor for cognitive radio network, Proc. - IEEE Int. Symp. Circuits Syst., 2020 (2020) 2–6. doi:10.1109/iscas45731.2020.9180415.
 L. S. Cardoso, M. Debbah, P. Bianchi, and J. Najim, Cooperative spectrum sensing using random matrix theory, 3rd ISWPC 2008, Proc., 2008, 334–338. doi: 10.1109/ISWPC.2008.4556225.
 W. Zhao, H. Li, M. Jin, Y. Liu, and S. J. Yoo, Enhanced detection algorithms based on eigenvalues and energy in random matrix theory paradigm, IEEE Access, 8 (2020) 9457–9468. doi: 10.1109/ACCESS.2020.2963935.
 Z. Chen, H. Wang, Z. Sun, R. Sun, X. Ning, and L. Ren, Two Novel Spectrum Sensing Algorithms Based on Eigenvalue under Different Noise, ICSP, 2020 (2020)- 428–432.doi: 10.1109/ICSP48669.2020.9321028.
 Q. Chen, P. Wan, Y. Wang, J. Li, and Y. Xiao, Research on cognitive radio spectrum sensing method based on information geometry, Lect. Notes Comput. Sci., 10603 (2017) LNCS 554–564. doi: 10.1007/978-3-319-68542-7_47.
 F. Awin, N. Salout, and E. Abdel-Raheem, Combined fusion rules in cognitive radio networks using different threshold strategies, Appl. Sci., 9 (2019).doi: 10.3390/app9235080.
 R. Sarikhani and F. Keynia, Cooperative Spectrum Sensing Meets Machine Learning: Deep Reinforcement Learning Approach, IEEE Commun. Lett., 24 (2020) 1459–1462. doi: 10.1109/LCOMM.2020.2984430.
 S. Zhang, Y. Wang, J. Li, P. Wan, Y. Zhang, and N. Li, A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm, Eurasip J. Wirel. Commun. Netw., 2019 (2019).doi:10.1186/s13638-019-1338-z.
 P. Shachi, K. R. Sudhindra, and M. N. Suma, Deep Learning for Cooperative Spectrum Sensing, in 2020 2nd PhD EDITS 2020, 2020, 20–21. doi: 10.1109/PhDEDITS51180.2020.9315306.
 A. Shirolkar and S. V. Sankpal, Deep Learning Based Performance of Cooperative Sensing in Cognitive Radio Network, 2021 2nd GCAT 2021, 2021. doi: 10.1109/GCAT52182.2021.9587617.
 V. Kumar, D. C. Kandpal, M. Jain, R. Gangopadhyay, and S. Debnath, K-mean clustering based cooperative spectrum sensing in generalized κ-μ Fading channels, 2016 22nd NCC 2016. doi: 10.1109/NCC.2016.7561130.
 Y. Cao and H. Pan, Energy-efficient cooperative spectrum sensing strategy for cognitive wireless sensor networks based on particle swarm optimization, IEEE Access, 8 (2020) 214707–214715. doi:10.1109/ACCESS.2020.3037707.
 S. Zhang, Y. Wang, P. Wan, J. Zhuang, Y. Zhang, and Y. Li, Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing, IEEE Access.8 (2020) 5777–5786. doi: 10.1109/ACCESS.2019.2963512.
 H. A. R. Akkar, W. A. H. Hadi, I. H. Al-Dosari, S. M. Saadi, and A. I. Ali, Classification accuracy enhancement based machine learning models and transform analysis, Commun. - Sci. Lett. Univ. Zilina, 23 (2021) C44–C53. doi:10.26552/COM.C.2021.2.C44-C53.
 A. A. Radhi, H. N. Abdullah, and H. A. R. Akkar, Denoised Jarque-Bera features-based K-Means algorithm for intelligent cooperative spectrum sensing, Digit. Signal Process. A Rev. J., 129 (2022) 1–15. doi:10.1016/j.dsp.2022.103659.
 A. A. Radhi, H. A. R. Akkar, and H. N. Abdullah, Skewness and access kurtosis as denoised mixed features-based K-Medoids for cooperative spectrum sensing, Phys. Commun., 54 (2022) 1–15. doi:10.1016/j.phycom.2022.101831.
 A. Martian, F. Lucian Chiper, O. Mohammed Khodayer Al-Dulaimi, M. Jalal Ahmad Al Sammarraie, C. Vladeanu, and I. Marghescu, Comparative Analysis of Software Defined Radio Platforms for Spectrum Sensing Applications, 2020 13th COMM 2020 Proc., 2020, 369–374.doi: 10.1109/COMM48946.2020.9142024.
 D. M. Molla, H. Badis, L. George, and M. Berbineau, Software Defined Radio Platforms for Wireless Technologies, IEEE Access, 10 (2022) 26203–26229.doi: 10.1109/ACCESS.2022.3154364.
 M. V. Lipski, S. Kompella, and R. M. Narayanan, Practical Implementation of Adaptive Threshold Energy Detection using Software Defined Radio, IEEE Trans. Aerosp. Electron. Syst., 57 (2021) 1227–1241. doi:10.1109/TAES.2020.3040059.
 H. Ben Thameur and I. Dayoub, Real-Time In-Lab Test of Eigenvalue-Based Spectrum Sensing Using USRP RIO SDR Boards, IEEE Commun. Lett., 25 (2021) 1029–1032. doi: 10.1109/LCOMM.2020.3037010.
 A. Mate, K. H. Lee, and I. T. Lu, “Spectrum sensing based on time covariance matrix using GNU radio and USRP for cognitive radio,” in 2011 IEEE LISAT 2011, 2–7.doi: 10.1109/LISAT.2011.5784217.
 A. Ivanov, A. Mihovska, K. Tonchev, and V. Poulkov, Real-time adaptive spectrum sensing for cyclostationary and energy detectors, IEEE Aerosp. Electron. Syst. Mag., 33 (2018) 20–33. doi: 10.1109/MAES.2018.170098.
 S. Majumder, Energy Detection Spectrum Sensing on RTL-SDR based IoT Platform, CICT 2018, 1–6, 2018, doi:10.1109/INFOCOMTECH.2018.8722360.
 M. Saber, A. El Rharras, R. Saadane, A. Chehri, N. Hakem, and H. A. Kharraz, Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms, Procedia Comput. Sci., 176 (2020) 2404–2413. doi: 10.1016/j.procs.2020.09.311.
 C. Gravelle and R. Zhou, SDR demonstration of signal classification in real-time using deep learning, GC Wkshps 2019 - Proc., 2019, doi: 10.1109/GCWkshps45667.2019.9024661.
 J. Zhuang, Y. Wang, P. Wan, S. Zhang, and Y. Zhang, Centralized spectrum sensing based on covariance matrix decomposition and particle swarm clustering, Phys. Commun., 46 (2021) 101322. doi:10.1016/j.phycom.2021.101322.
 S. Zhang, Y. Wang, Y. Zhang, P. Wan, and J. Zhuang, A Novel Clustering Algorithm Based on Information Geometry for Cooperative Spectrum Sensing, IEEE Syst. J.,(2020) 1–10.doi: 10.1109/jsyst.2020.3001407.
 L. A. N. Man, S. Committee, and I. Computer, The Institute of Electrical and Electronics Engineering, Inc, Std. IEEE 802.22, (WRAN). 2020.
 J. Liang, M. L. Tang, and X. Zhao, Testing high-dimensional normality based on classical skewness and Kurtosis with a possible small sample size, Commun. Stat. - Theory Methods, 48 (20195719–5732. doi:10.1080/03610926.2018.1520882.
 R. Kumar, R. K. D. Harishchandra, and D. V. P. Singh, Applications of Advanced Computing in Systems. Singapore: Springer Nature Singapore Pte Ltd, 2021.
 K. Vachhani and R. A. Mallari, Experimental study on wide band FM receiver using GNURadio and RTL-SDR, ICACCI. 2015, 2015, 1810–1814. doi: 10.1109/ICACCI.2015.7275878.
 J. Talukdar, B. Mehta, K. Aggrawal, and M. Kamani, Implementation of SNR estimation based energy detection on USRP and GNU radio for cognitive radio networks, Proc. 2017 Int. Conf. Wirel. Commun. Signal Process. Networking, 2018,2018,304–308. doi: 10.1109/WiSPNET.2017.8299767.
 X. Zhu, C. X. Wang, J. Huang, M. Chen, and H. Haas, A Novel 3D Non-Stationary Channel Model for 6G Indoor Visible Light Communication Systems, IEEE Trans. Wirel. Commun., 21 (2022) 1–16.doi: 10.1109/TWC.2022.3165569.
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