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http://dx.doi.org/10.3837/tiis.2021.06.011

Improved Convolutional Neural Network Based Cooperative Spectrum Sensing For Cognitive Radio  

Uppala, Appala Raju (Department of ECE, Jawaharlal Nehru Technological University, Geethanjali College of Engineering and Technology)
Narasimhulu C, Venkata (LORDS Institute of Engineering and Technology)
Prasad K, Satya (Vignan's Foundation for Science, Technology & Research)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.6, 2021 , pp. 2128-2147 More about this Journal
Abstract
Cognitive radio systems are being implemented recently to tackle spectrum underutilization problems and aid efficient data traffic. Spectrum sensing is the crucial step in cognitive applications in which cognitive user detects the presence of primary user (PU) in a particular channel thereby switching to another channel for continuous transmission. In cognitive radio systems, the capacity to precisely identify the primary user's signal is essential to secondary user so as to use idle licensed spectrum. Based on the inherent capability, a new spectrum sensing technique is proposed in this paper to identify all types of primary user signals in a cognitive radio condition. Hence, a spectrum sensing algorithm using improved convolutional neural network and long short-term memory (CNN-LSTM) is presented. The principle used in our approach is simulated annealing that discovers reasonable number of neurons for each layer of a completely associated deep neural network to tackle the streamlining issue. The probability of detection is considered as the determining parameter to find the efficiency of the proposed algorithm. Experiments are carried under different signal to noise ratio to indicate better performance of the proposed algorithm. The PU signal will have an associated modulation format and hence identifying the presence of a modulation format itself establishes the presence of PU signal.
Keywords
Cognitive radio; Cooperative spectrum sensing; Primary user; Simulated annealing; Neural network;
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1 W. Lee, M. Kim, and D. H. Cho, "Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks," IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 3005-3009, March 2019.   DOI
2 X. Lu, P. Wang, D. Niyato, and E. Hossain, "Dynamic Spectrum Access in Cognitive Radio Networks with RF Energy Harvesting," IEEE Wirel. Communication, vol. 21, no. 3, pp. 102-110, 2014.   DOI
3 J. Mitola and G. Q. Maguire, "Cognitive radio: Making software radios more personal," IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, August 1999.   DOI
4 J. B. Wei, S. Wang and H. T. Zhao, "Cognitive wireless networks: key techniques and sate of the art," Journal on Communications, vol. 32, no. 11, pp. 147-158, 2011.
5 Xianghui Cao, Xiangwei Zhou, Lu Liu, and Yu Cheng, "Energy-Efficient Spectrum Sensing for Cognitive Radio Enabled Remote State Estimation Over Wireless Channels," IEEE Transactions on Wireless Communications, vol. 14, no. 4, April 2015.
6 Z. Sun, Q. Wang, and C. Che, "Study of Cognitive Radio Spectrum Detection in OFDM System," in Proc. of 2010 Asia-Pacific Conference on Wearable Computing Systems, 2010.
7 Vaibhav Kumar, Deep Chandra Kandpal, and Monika Jain, "K-mean Clustering based Cooperative SpectrumSensing in Generalized_-μ Fading Channels," in Proc. of 2016 Twenty Second National Conference on Communication (NCC), 2016.
8 A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Proc. of NIPS Stateline NV, pp. 1097-1105, December 2012.
9 Jiandong Xie, Jun Fang, Chang Liu, Xuanheng Li, "Deep Learning-Based Spectrum Sensing in Cognitive Radio: A CNN-LSTM Approach," IEEE Communications Letters, vol. 24, no. 10, pp. 2196-2200, 2020.   DOI
10 S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, May 1983.   DOI
11 V. Kuppusamy, and R. Mahapatra, "Primary user detection in OFDM based MIMO Cognitive Radio," in Proc. of 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2008.
12 M. Lakshmi, R.Saravanan, and R.Muthaiah, "Energy Detection Based Spectrum Sensing For Cognitive Radio Using Fusion Rules," International Journal of Scientific & Engineering Research, vol. 4, no. 6, June 2013.   DOI
13 M. Langkvist L. Karlsson, A. Loutfi, "A review of unsupervised feature learning and deep learning for time-series modelling," Pattern Recognit, Lett., vol. 42, pp.11-24, 2014.   DOI
14 L. Xie and A. Yuille, "Genetic CNN," in Proc. of the IEEE International Conference on Computer Vision, pp. 1388-1397, 2017.
15 Wangjam Niranjan Singh and Ningrinla Marchang, "A Review on Spectrum Allocation in Cognitive Radio Network," International Journal of Communication Networks and Distributed Systems, vol.23, no.2, pp.172 - 193, March 2018.
16 Wei Li, Kai Liu, Lin Yan, Fei Cheng, YunQiu Lv, and LiZhe Zhang, "FRD-CNN: Object detection based on small-scale convolutional neural networks and feature reuse," Scientific Reports, vol. 9, no. 1, 2019.
17 T. Xu, M. Zhang, and H. Hu, "Harmonious Coexistence of Heterogeneous Wireless Networks in Unlicensed Bands: Solutions from the Statistical Signal Transmission Technique," IEEE Vehicle. Tech. Mag, vol. 14, no. 2, pp. 61-69, June 2019.   DOI
18 J. Xie, J. Fang, C. Liu, and X. Li, "Deep Learning-Based Spectrum Sensing in Cognitive Radio: A CNN-LSTM Approach," IEEE Communications Letters, 2020.
19 R. G. Yelalwar, and Y. Ravinder, "Artificial Neural Network Based Approach for Spectrum Sensing in Cognitive Radio," in Proc. of International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2018.
20 A. Al-Tahmeesschi, M. Lopez-Benitez, J. Lehtomaki, and K. Umebayashi, "Investigating the Estimation of Primary Occupancy Patterns under Imperfect Spectrum Sensing," in Proc. of 2017 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2017.
21 S. Badrinath, and V. U. Reddy, "A hybrid energy detection approach to spectrum sensing," in Proc. of 2009 First UK-India International Workshop on Cognitive Wireless Systems (UKIWCWS), December 2009.
22 C. Clancy, J. Hecker, E. Stuntebeck, and T. O'Shea, "Applications of machine learning to cognitive radio networks," IEEE Wireless Communication, vol. 14, no. 4, pp. 47-52, August 2007.   DOI
23 Z. C. Lipton, J. Berkowitz, and C. Elkan, "A critical review of recurrent neural networks for sequence learning," 2015.
24 C. Liu, J. Wang, X. Liua, and Y. C. Liang, "Deep CM-CNN for Spectrum Sensing in Cognitive Radio," IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2306-2321, October 2019.   DOI
25 S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput., vol. 9, no. 8, pp. 1735-1780, November 1997.   DOI
26 A. Agarwal, S. Dubey, M. A. Khan, R. Gangopadhyay, and S. Debnath, "Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access," in Proc. of SPCOM, pp. 1-5, June 2016.
27 Kai Yang, Zhitao Huang, Xiang Wang and Xueqiong Li, "A Blind Spectrum Sensing Method Based on Deep Learning," Sensors, vol. 19, no. 10, pp. 2270, March 2019.   DOI
28 Chun-Wei Tsai, Chien-Hui Hsia, Shuang-Jie Yang, Shih-Jui Liu and F. Zhi-Yan, "Optimizing hyperparameters of deep learning in predicting bus passengers based on simulated annealingang," Applied Soft Computing Journal, vol. 88, 2020.
29 Dhaval Patel and Yogesh Trivedi, "Non-parametric Blind Spectrum Sensing Based on Censored Observations for Cognitive radio," Journal of Signal Processing Systems, vol. 78, pp. 275-281, March 2015.   DOI
30 A. Ghasemi, E. S. Sousa, "Collaborative spectrum sensing for opportunistic access in fading environments," in Proc. of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '05), pp. 131-136, November 2005.
31 Saber Mohammed, El Rharras Abdessamad, and Saadane Rachid, "An Optimized Spectrum Sensing Implementation based on SVM, KNN and TREE Algorithms," in Proc. of International Conference on Signal-Image Technology & Internet-Based Systems, 2019.
32 L. Deng, "A tutorial survey of architectures, algorithms, and applications for deep learning," APSIPA Trans. Signal Inform. Process, vol.3, pp.1-29, 2014.
33 Mingdong Xu, Zhendong Yin, and Mingyang Wu, "Spectrum Sensing Based on Parallel CNN-LSTM Network," in Proc. of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020.
34 Ouchra Senadji and Kevin Chang, "Detection of dynamic primary user with cooperative spectrum sensing," in Proc. of 21st European Signal Processing Conference (EUSIPCO 2013), May 2014.
35 P. Sharma and V. Abrol, "Individual vs cooperative spectrum sensing for Cognitive Radio Networks," in Proc. of 2013 Tenth International Conference on Wireless and Optical Communications Networks (WOCN), 2013.
36 S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, 2005.   DOI
37 C. W. Tsai, C. H. Hsia, S. J. Yang, S. J. Liu, and Z. Y. Fang, "Optimizing hyper parameters of deep learning in predicting bus passengers based on simulated annealing," Applied Soft Computing Journal, vol. 88, no. 106068, 2020.
38 H, S. Reyes, N. Subramaniam, N. Kaabouch, and W. Chen, "A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks," Comput. Electronics journal Eng., vol. 52, pp. 319-327, 2016.   DOI
39 Shree Krishna Sharma, Mohammad Patwary, Symeon Chatzinotas, Bjorn Ottersten, and Mohamed Abdel-Maguid, "Repeater for 5G Wireless: A Complementary Contender for Spectrum Sensing Intelligence,".
40 K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv, pp.1409.1556, 2014.
41 Mangesh V Deshmukh , Dr. Mrs. Shruti Oza, "Spectrum Sensing based on Energy Detection for Cognitive Radio using FPGA," vol. 08, no. 05, May 2019.
42 Wei Ma, Mu Qing Wu, Dong Liu, and Meng Ling Wang, "User sensing based on MIMO cognitive radio sensor networks," in Proc. of 2009 2nd IEEE International Conference on Computer Science and Information Technology, 2009.
43 W. Huixin, M. Duo, and L. He, "Analysis and Simulation of the Dynamic Spectrum Allocation Based on Parallel Immune Optimization in Cognitive Wireless Networks," The Scientific World Journal, pp. 1-8, 2014.
44 Hurmat Ali Shah and Insoo Koo, "Reliable Machine Learning Based Spectrum Sensing inCognitive Radio Networks," Wireless Communications and Mobile Computing, 2018.
45 Patel D. K. and Y. N. Trivedi, "LRS-G2G2 Based Non-parametric Spectrum Sensing for Cognitive Radio," in Proc. of International Conference on Cognitive Radio Oriented Wireless Networks, vol. 172, pp 330-341, 2014.
46 Karaputugala Madushan Thilina, Kae Won Choi, Nazmus Saquib, and Ekram Hossain, "Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks," IEEE Journal on selected areas in communications, vol. 31, no. 11, pp. 2209-2221 November 2013.   DOI
47 Y. J. Tang, Q. Y. Zhang, and W. Lin, "Artificial Neural Network Based Spectrum Sensing Method for Cognitive Radio," in Proc. of 2010 International Conference on Computational Intelligence and Software Engineering, 2010.
48 H. M. A. Abdullah, and A. V. S. Kumar, "HFSA-SORA: Hybrid firefly simulated annealing based spectrum opportunistic routing algorithm for Cognitive Radio Ad hoc Networks (CRAHN)," in Proc. of 2017 International Conference on Intelligent Computing and Control (I2C2), 2017.
49 H. Wang, E. H. Yang, Z. Zhao, and W. Zhang, "Spectrum sensing in cognitive radio using goodness of the testing," IEEE Trans.Wireless Communication, vol. 8, no. 11, pp. 5427-5430, November 2009.   DOI
50 B. Wang and K. J. R. Liu, "Advances in cognitive radio networks: a survey," IEEE Journal on Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5-23, 2011.   DOI
51 F. Azmat, Y. Chen, and N. Stocks, "Analysis of spectrum occupancy using machine learning algorithms," IEEE Trans. Veh. Technol, vol. 65, no. 9, pp. 6853-6860, September 2016.   DOI