Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks
DOI:
https://doi.org/10.26636/jtit.2022.164922Keywords:
cognitive radio, improved NMF, LU-SLNO system, optimized CNN, spectrum sensingAbstract
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.
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[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.
View in Google Scholar
[2] L. A. Vasquez-Toledo, B. Borja-Benítez, and J. A. Tirado-Mendez, “Mathematical analysis of highly scalable cognitive radio systems using hybrid game and queuing theory”, AEU – International Journal of Electronics and Communications, vol. 127, Article no. 153406, 2020.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[7] R. Sekaran, S. N. Goddumarri, and D. Gupta, “5G integrated spectrum selection and spectrum access using AI-based framework for IoT-based sensor networks”, Computer Networks, vol. 186, 2020.
View in Google Scholar
[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.
View in Google Scholar
[9] S. N. Kumar and K. Bikshalu, “An extensive survey on cognitive radio-based spectrum sensing using different algorithms”, Materials Today: Proceedings, 2020.
View in Google Scholar
[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.
View in Google Scholar
[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 no. 102390, 2020.
View in Google Scholar
[12] G. P. Aswathy, K. Gopakumar, and T. P. Imthias Ahamed, “Joint sub-Nyquist wideband spectrum sensing and reliable data transmission for cognitive radio networks over white space”, Digital Signal Processing, vol. 101, Article no. 102713, 2020.
View in Google Scholar
[13] E. Geoffrey and T. Shankar, “Hybrid PSO-GSA for energy-efficient spectrum sensing in cognitive radio network”, Physical Communication, vol. 40, Article no. 101091, 2020.
View in Google Scholar
[14] M. A. Osameh, M. A. Hisham, and A.-M. Haithem, “Efficient on-demand spectrum sensing in sensor-aided cognitive radio networks”, Computer Communications, vol. 156, pp. 11–24, 2020.
View in Google Scholar
[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 no. 153101, 2020.
View in Google Scholar
[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 no. 102289, 2020.
View in Google Scholar
[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.
View in Google Scholar
[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 no. 152876, 2019.
View in Google Scholar
[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.
View in Google Scholar
[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 no. 101139, 2020.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[24] P. Vijayakumar and S. Malarvizhi, “Wide band full duplex spectrum sensing with self-interference cancellation – an efficient SDR implementation”, Mobile Netw. Appl., vol. 22, pp. 702–711, 2017.
View in Google Scholar
[25] E. Geoffrey and T. Shankar, “Hybrid PSO-GSA for energy-efficient spectrum sensing in cognitive radio network”, Physical Communication, vol. 40, Article no. 101091, 2020.
View in Google Scholar
[26] M. Saber et al., “Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms”, Procedia Computer Science, vol. 176, pp. 2404–2413, 2020.
View in Google Scholar
[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, pp. 273–278, 2012.
View in Google Scholar
[28] A. Ijaz, A. B. Awoseyila, and B. G. Evans, “Improved SNR estimation for BPSK and QPSK signals”, Electron. Lett., vol. 45, no. 16, 2009.
View in Google Scholar
[29] J. Gu et al., “Recent advances in convolutional neural networks”, Pattern Recognition, vol. 77, pp. 354–377, 2018.
View in Google Scholar
[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. 10, no. 5, pp. 388–395, 2019.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[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”, in Proc. IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013), 2013.
View in Google Scholar
[34] G. Aloysius and B. R. Rajakumar, “APOGA: An adaptive population pool size-based genetic algorithm”, AASRI Procedia, vol. 4, pp. 288–296, 2013.
View in Google Scholar
[35] B. R. Rajakumar and G. Aloysius, “A new adaptive mutation technique for genetic algorithm”, in Proc. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2012, pp. 1–7.
View in Google Scholar
[36] B. W. Mukund and N. Gomathi, “Improved GWO-CS algorithm-based optimal routing strategy in VANET”, Journal of Networking and Communication Systems, vol. 2, no. 1, pp. 34–42, 2019.
View in Google Scholar
[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.
View in Google Scholar
[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.
View in Google Scholar
[39] M. H. Omar, H. Suhaidi, and A. N. Shahrudin, “Eigenvalue-based signal detectors performance comparison”, in Proc. 17th Asia-Pacific Conf. on Commun., 2011.
View in Google Scholar
[40] Y. Zeng and Y.-C. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio”, IEEE Trans. Commun., vol. 57, no. 6, pp. 1784–1793, 2009.
View in Google Scholar
[41] M. H. Omar, H. Suhaidi, A. Angela, and A. N. Shahrudin, “SVD-based signal detector for cognitive radio networks”, in Proc. 13th Int. Conf. on Computer Modelling and Simulation, 2011.
View in Google Scholar
[42] Y. Zeng and Y.-C. Liang, “Maximum-minimum eigenvalue detection for cognitive radio”, in Proc. IEEE 18th Int. Symp. on Personal, Indoor and Mobile Radio Commun., 2007, pp. 1–5.
View in Google Scholar
[43] Y. Liu et al., “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, Article no. e2929, 2017.
View in Google Scholar
[44] RadioML 2016 dataset [Online]. Available: https://www.kaggle.com/datasets/nolasthitnotomorrow/radioml2016-deepsigcom?resource=download.
View in Google Scholar
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