Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

• Autorzy: Jeevangi Sanjeevkumar , Jawaligi Shivkumar , Patil Vilaskumar

Identyfikatory

DOI

10.26636/jtit.2022.164922

• Języki publikacji: EN

Abstrakty

EN

Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.

Słowa kluczowe

EN

cognitive radio; improved NMF; LU-SLNO system; optimized CNN; spectrum sensing

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2022
• Tom: nr 4
• Strony: 21--32
• Opis fizyczny: Bibliogr. 44 poz., rys., wykr.

Twórcy

autor

Jeevangi Sanjeevkumar

• Department of Electronics and Communication Engineering, Faculty of Engineering and Technology (Co-Ed), Sharnbasva University, India

autor

Jawaligi Shivkumar

• Department of Electronics and Communication Engineering, Faculty of Engineering and Technology (Co-Ed), Sharnbasva University, India

autor

Patil Vilaskumar

• Department of Electronics and Communication Engineering, Faculty of Engineering and Technology (Co-Ed), Sharnbasva University, India

Bibliografia

• [1] S. Thompson, A-T. Fadi, K.J. Suresh, N. Balamurugan, and S. Sweta, “Artificial intelligence inspired energy and spectrum aware cluster based routing protocol for cognitive radio sensor networks”, Journal of Parallel and Distributed Computing, vol. 142, pp. 90–105, 2020 (DOI: 10.1016/j.jpdc.2020.04.007).
• [2] L.A. Vasquez-Toledo, B. Borja-Benítez, J. Alfredo, and TiradoMendez, “Mathematical analysis of highly scalable cognitive radio systems using hybrid game and queuing theory”, AEU – International Journal of Electronics and Communications, vol. 127, Article 153406, 2020 (DOI: 10.1016/j.aeue.2020.153406).
• [3] E.G. Carla, R.C. Mario, and K. Insoo, “Relay selection and power allocation for secrecy sum rate maximization in underlying cognitive radio with cooperative relaying NOMA”, Neurocomputing, 2021 (DOI: 10.1016/j.neucom.2020.08.082).
• [4] G. Kamlesh, S.N. Merchant, and U.B. Desai, “A novel multistage decision fusion for cognitive sensor networks using AND and OR rules”, Digital Signal Processing, vol. 42, pp. 27–34, 2015 (DOI: 10.1016/j.dsp.2015.04.).
• [5] J. Qadir, A. Baig, A. Ali, and Q. Shafi, “Multicasting in cognitive radio networks: Algorithms, techniques and protocols”, Journal of Network and Computer Applications, vol. 45, pp. 44–61, 2014 (DOI: 10.1016/j.jnca.2014.07.024).
• [6] S.M. Budaraju and M.A. Bhagyaveni, “A novel energy detection scheme based on channel state estimation for cooperative spectrum sensing”, Computers and Electrical Engineering, vol. 57, pp. 176– 185, 2017 (DOI: 10.1016/j.compeleceng.2016.08.017).
• [7] R. Sekaran, S.N. Goddumarri, and D. Gupta, “5G Integrated Spectrum Selection and Spectrum Access using AI-based Frame work for IoT based Sensor Networks”, Computer Networks, vol. 186, 2020 (DOI: 10.1016/j.comnet.2020.107649).
• [8] A. Kumar, S. Saha, and R. Bhattacharya, “Wavelet transform based novel edge detection algorithms for wideband spectrum sensing in CRNs”, AEU – International Journal of Electronics and Communications, vol. 84, pp. 100–110, 2017 (DOI: 10.1016/j.aeue.2017.11.024).
• [9] S.N. Kumar and K. Bikshalu, “An extensive survey on cognitive radio based spectrum sensing using different algorithms”, Materials Today: Proceedings, 2020 (DOI: 10.1016/j.matpr.2020.11.482).
• [10] P. Feng, Y. Bai, and C. Liu, “A rapid coarse-grained blind wideband spectrum sensing method for cognitive radio networks”, Computer Communications, vol. 166, pp. 234–243, 2020 (DOI: 10.1016/j.comcom.2020.11.015).
• [11] R. Ahmed, Y. Chen, and L. Du, “CR-IoTNet: Machine learning based joint spectrum sensing and allocation for cognitive radio enabled IoT cellular networks”, Ad Hoc Networks, vol. 112, Article 102390, 2020 (DOI: 10.1016/j.adhoc.2020.102390).
• [12] G.P. Aswathy, K. Gopakumar, and T.P. Imthias Ahamed, “Joint subNyquist wideband spectrum sensing and reliable data transmission for cognitive radio networks over white space”, Digital Signal Processing, vol. 101, Article 102713, 2020 (DOI: 10.1016/j.dsp.2020.102713). [
• [13] E. Geoffrey and T. Shankar, “Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network”, Physical Communication, vol. 40, Article 101091, 2020 (DOI: 10.1016/j.phycom.2020.101091).
• [14] M.A. Osameh, M.A. Hisham, and A-M. Haithem, “Efficient ondemand spectrum sensing in sensor-aided cognitive radio networks”, Computer Communications, vol. 156, pp. 11–24, 2020 (DOI: 10.1016/j.comcom.2020.03.032).
• [15] K. Alok, T. Prabhat, and G. Singh, “Threshold selection and cooperation in fading environment of cognitive radio network: Consequences on spectrum sensing and throughput”, AEU – International Journal of Electronics and Communications, vol. 117, Article 153101, 2020 (DOI: 10.1016/j.aeue.2020.153101).
• [16] Y. Gao, C. Wang, and X. Bai, “GLRT-based spectrum sensing by exploiting multitaper spectral estimation for cognitive radio network”, Ad Hoc Networks, vol. 109, Article 102289, 2020 (DOI: 10.1016/j.adhoc.2020.102289).
• [17] K.G. Walid and S. Suzan, “Performance evaluation of cooperative eigenvalue spectrum sensing GLRT under different impulsive noise environments in cognitive radio”, Computer Communications, vol. 160, pp. 567–576, 2020 (DOI: 10.1016/j.comcom.2020.06.033).
• [18] K. Mourougayane, B. Amgothu, and S. Srikanth, “A robust multistage spectrum sensing model for cognitive radio applications”, AEU – International Journal of Electronics and Communications, vol. 110, Article 152876, 2019 (DOI: 10.1016/j.aeue.2019.152876).
• [19] M. Aloqaily, H. Bany, S. Jalel, and B. Othman, “A multi-stage resource-constrained spectrum access mechanism for cognitive radio IoT networks: Time-spectrum block utilization”, Future Generation Computer Systems, vol. 110, pp. 254–266, 2020 (DOI: 10.1016/j.future.2020.04.022).
• [20] J.B. Patel, S. Collins, and B. Sirkeci-Mergen, “A framework to analyze decision strategies for multi-band spectrum sensing in cognitive radios”, Physical Communication, vol. 42, Article 101139, 2020 (DOI: 10.1016/j.phycom.2020.101139).
• [21] H. Qing, H. Li, and Y. Liu, “Multistage Wiener filter aided MDL approach for wideband spectrum sensing in cognitive radio networks”, AEU – International Journal of Electronics and Communications, vol. 73, pp. 165–172, 2017 (DOI: 10.1016/j.aeue.2017.01.012).
• [22] S. Kim, “Inspection game based cooperative spectrum sensing and sharing scheme for cognitive radio IoT system”, Computer Communications, vol. 105, pp. 116–123, 2017 (DOI: 10.1016/j.comcom.2017.01.015).
• [23] R.S. Rajput, R. Gupta, and A. Trivedi, “An Adaptive Covariance Matrix Based on Combined Fully Blind Self Adapted Method for Cognitive Radio Spectrum Sensing”, Wireless Pers Commun. vol. 114, pp. 93–111, 2020 (DOI: 10.1007/s11277-020-07352-9).
• [24] P. Vijayakumar, S.W. Malarvizhi, “Wide band Full Duplex Spectrum Sensing with Self-Interference Cancellation-An Efficient SDR Implementation”, Mobile Netw Appl 22, pp. 702–711, 2017 (DOI: 10.1007/s11036-017-0844-7).
• [25] E. Geoffrey and T. Shankar. “Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network”, Physical Communication, vol. 40 101091, 2020 (DOI: 10.1016/j.phycom.2020.101091).
• [26] 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 Computer Science, vol. 176, pp. 2404–2413, 2020 (DOI: 10.1016/j.procs.2020.09.311).
• [27] M. Taleb, A. Amei, M. Jian, and J. Yingtao, “On a new approach to SNR estimation of BPSK signals”, Int J Electron Telecommun, vol. 58, no. 3, 273–278, 2012 (DOI: 10.2478/v10177-012-0038-).
• [28] A. Ijaz, A.B. Awoseyila, and B.G. Evans, “Improved SNR estimation for BPSK and QPSK signals”, IEEE Electron Lett, vol. 45, 16 (DOI: 10.1049/el.2009.1759).
• [29] J. Gu, et. al., “Recent advances in convolutional neural networks”, Pattern Recognition, vol. 77, pp. 354–377, 2018 (DOI: 10.1016/j.patcog.2017.10.013).
• [30] R.M.T. Masadeh, B.A. Mahafzah, and A.-A. Sharieh, “Sea Lion Optimization Algorithm”, International Journal of Advanced Computer Science and Applications, vol. no. 10, pp. 388–395, 2019 (DOI: 10.14569/IJACSA.2019.0100548).
• [31] B.R. Rajakumar, “Impact of Static and Adaptive Mutation Techniques on Genetic Algorithm”, International Journal of Hybrid Intelligent Systems, vol. 10, no. 1, pp. 11–22, 2013 (DOI: 10.3233/HIS-120161).
• [32] B.R. Rajakumar, “Static and Adaptive Mutation Techniques for Genetic algorithm: A Systematic Comparative Analysis”, International Journal of Computational Science and Engineering, vol. 8, no. 2, pp. 180–193, 2013 (DOI: 10.1504/IJCSE.2013.053087).
• [33] S.M. Swamy, B.R. Rajakumar, and I.R. Valarmathi, “Design of Hybrid Wind and Photovoltaic Power System using Opposition-based Genetic Algorithm with Cauchy Mutation”, IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013), 2013, (DOI: 10.1049/ic.2013.0361).
• [34] G. Aloysius and B.R. Rajakumar, “APOGA: An Adaptive Population Pool Size based Genetic Algorithm”, AASRI Procedia – 2013 AASRI Conference on Intelligent Systems and Control (ISC 2013), vol. 4, pp. 288–296, 2013, (DOI: 10.1016/j.aasri.2013.10.043).
• [35] B.R. Rajakumar and G. Aloysius, “A New Adaptive Mutation Technique for Genetic Algorithm”, In proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–7, 2012 (DOI: 10.1109/ICCIC.2012.6510293).
• [36] B.W. Mukund and N. Gomathi, “Improved GWO-CS AlgorithmBased Optimal Routing Strategy in VANET”, Journal of Networking and Communication Systems, vol. 2, no. 1, pp. 34–42, 2019 (DOI: 10.46253/jnacs.v2i1.a4).
• [37] H.B. Sadashiv, S.F. Kodad, S.K. Ambekar, and D. Manjunath, “Enhanced Invasive Weed Optimization Algorithm with Chaos Theory for Weightage based Combined Economic Emission Dispatch”, Journal of Computational Mechanics, Power System and Control, vol. 2, no. 3, pp. 19–27, 2019 (DOI: 10.46253/jcmps.v2i3.a3).
• [38] N.J. Amolkumar and N. Gomathi, “DIGWO: Hybridization of Dragonfly Algorithm with Improved Grey Wolf Optimization Algorithm for Data Clustering”, Multimedia Research, vol. 2, no. 3, pp. 1–11, 2019 (DOI: 10.46253/j.mr.v2i3.a1).
• [39] M.H. Omar, H. Suhaidi, and A.N. Shahrudin, “Eigenvaluebased signal detectors performance comparison”, The 17th Asia Pacific conference on communications, 2011 (DOI: 10.1109/APCC.2011.6477853).
• [40] Z. Yonghong and C.L. Ying, “Eigenvalue-based spectrum sensing algorithms for cognitive radio”, IEEE Trans Commun 2009, 57(6), pp. 1784–1793 (DOI: 10.1109/TCOMM.2009.06.070402).
• [41] M.H. Omar, H. Suhaidi, A. Angela, and A.N. Shahrudin, “SVD based signal detector for cognitive radio networks”, 13th International conference on computer modeling and simulation, 2011 (DOI: 10.1109/UKSIM.2011.104).
• [42] Z. Yonghong and C.L. Ying, “Maximum-minimum eigenvalue detection for cognitive radio”, IEEE eighteenth international symposium on personal, indoor and mobile radio communications, pp. 1–5 2007 (DOI: 10.1109/PIMRC.2007.4394211)
• [43] Y. Liu, Y. Liang, Q. Kuang, F. Xie, Y. Hao, Z. Wen, and M. Li, “Postmodified non-negative matrix factorization for deconvoluting the gene expression profiles of specific cell types from heterogeneous clinical samples based on RNA-sequencing data”, Journal of Chemometrics, e2929, 2017 (DOI: 10.1002/cem.2929).
• [44] –, https://www.kaggle.com/datasets/nolasthitnotomorr ow/radioml2016-deepsigcom?resource=download.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).