Abstract
This paper aims to enhance the effectiveness of the present spectrum and efficiency of cognitive radio and use the reinforcement learning model to enhance its security. It incorporates a concept that detects the presence of licensed primary users in a channel and assigns channels to secondary users automatically without the need for user intervention where the primary users are not present. An LDPC decoder used at the receiver’s end allows for error detection and correction considering situations where noisy channels manipulate data. The LDPC decoder and software portion of cognitive radio, that is implemented using the energy detection method, are done using LabVIEW software and the reinforcement learning model which use the deep Q-learning algorithm is developed using Python.
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References
S. Seo, T.N. Mudge, Y. Zhu, C. Chaitali, Design and analysis of LDPC decoders for software defined radio (2007). https://doi.org/10.1109/SIPS.2007.4387546
N. Mugesh, R.J. Theivadas, S.K. Padmanabhan. LDPC encoder for ofdm based cognıtıve radıo (2014)
R. Anantharaman, K. Kwadiki, V. Rao, Hardware ımplementation analysis of min-sum decoders. Adv. Electr. Electron. Eng. 17 (2019). https://doi.org/10.15598/aeee.v17i2.3042
A. Rajagopal, K. Karibasappa, K.S. Vasundara Patel, Hardware implementation of modified SSD LDPC decoder. Int. J. Comput. Aided Eng. Technol. (IJCAET) Indersci. J. 14(3), 426–440. ISSN: 1757–2665
A. Rajagopal, K. Karibasappa, K.S. Vasundara Patel, Study of LDPC decoders with quadratic residue sequence for communication system, Int. J. Inf. Comput. Secur. (IJICS) Indersci. J. 13(1), 18–31. ISSN: 1744–1733
K.-E. Lee, J.G. Park, S.-J. Yoo, Intelligent cognitive radio Ad-Hoc network: planning. Learn. Dyn. Configuration Electron. 10, 254 (2021). https://doi.org/10.3390/electronics10030254
F. Salahdine, Spectrum Sensing Techniques For Cognitive Radio Networks (2017)
A. Nasser, H. Al Haj Hassan, J. Abou Chaaya, A. Mansour, K.-C. Yao, Spectrum sensing for cognitive radio: recent advances and future challenge. Sensors 21, 2408 (2021). https://doi.org/10.3390/s21072408
S. Dhivya, A. Rajeswari, R. Aswatha, Implementatıon of energy detectıon based spectrum sensıng ın NI USRP 2920 (2017)
R. Sowmiya, G. Sangeetha, Energy detection using NI USRP 2920 (2016)
M. Subhedar, G. Birajdar, Spectrum sensing techniques in cognitive radio networks: a survey. Int. J. Next-Gener.Netw. 3 (2011). https://doi.org/10.5121/ijngn.2011.3203
Evaluation of energy detection technique for spectrum sensing. Daniela Mercedes and Angel Gabriel
W. Ejaz, Spectrum sensıng ın cognıtıve radıo networks NUST-MS PhD-ComE-01 (2006)
C.S. Rawat, G.G. Korde, Comparison between energy detection and cyclostationary detection for transmitter section. Int. J. Electr. Electron. Data Commun. 3, 2320–2084 (2015)
J. Chen, A. Gibson, J. Zafar, Cyclostationary spectrum detection in cognitive radios, pp. 1–5 (2008). https://doi.org/10.1049/ic:20080398
M. Ling, K.-L. Yau, J. Qadir, G.S. Poh, Q. Ni, Application of reinforcement learning for security enhancement in cognitive radio networks. Appl. Soft Comput. 37 (2015). https://doi.org/10.1016/j.asoc.2015.09.017
A. Nasser, H. Al Haj Hassan, J.A. Chaaya, A. Mansour, K.-C. Yao, Spectrum sensing for cognitive radio: recent advances and future challenge. Sensors 21(7), 2408 (2021). https://doi.org/10.3390/s21072408
K.-L. Yau, G.S. Poh, S.F. Chien, H. Al-Rawi, Application of reinforcement learning in cognitive radio networks: models and algorithms. Sci. World J. 2014, 209810 (2014). https://doi.org/10.1155/2014/209810
F. Obite, A. Usman, E. Okafor, An overview of deep reinforcement learning for spectrum sensing in cognitive radio networks. Digital Sig. Process. 113, 103014 (2021). https://doi.org/10.1016/j.dsp.2021.103014
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Lalwani, P., Anantharaman, R. (2022). Reinforcement Learning for Security of a LDPC Coded Cognitive Radio. In: Raj, J.S., Kamel, K., Lafata, P. (eds) Innovative Data Communication Technologies and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 96. Springer, Singapore. https://doi.org/10.1007/978-981-16-7167-8_64
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DOI: https://doi.org/10.1007/978-981-16-7167-8_64
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