source allocation for spectrum underlay in cognitive radio net- works. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1-6, 2018. Bibliography 162 Y. Sharaf-Dabbagh and W. Saad. Transfer learning for device fingerprinting with application to cognitive radio networks. In 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pages 2138-2142, 2015. 163 Y. Shi, Y. E. Sagduyu, T. Erpek, K. Davaslioglu, Z. Lu, and J. H. Li. Adversarial deep learning for cognitive radio security: Jamming attack and defense strate- gies. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1-6, 2018. 164 S. Singh and A. Trivedi. Anti-jamming in cognitive radio networks using rein- forcement learning algorithms. In 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN), pages 1-5, 2012. 165 F. Slimeni, B. Scheers, Z. Chtourou, and V. Le Nir. Jamming mitigation in cogni- tive radio networks using a modified Q-learning algorithm. In 2015 International Conference on Military Communications and Information Systems (ICMCIS), pages 1-7, 2015. 166 F. Slimeni, B. Scheers, Z. Chtourou, V. Le Nir, and R. Attia. Cognitive radio jamming mitigation using Markov decision process and reinforcement learning. Procedia Computer Science, 73:199-208, 2015. 167 Q. H. Spencer, A. L. Swindlehurst, and M. Haardt. Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE Transactions on Signal Processing, 52(2):461-471, 2004. 168 S. Srinivasan, K. B. Shivakumar, and M. Mohammad. Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks. International Journal of Dis- tributed Sensor Networks, 15(9), 2019. doi: 10.1177/1550147719860365. 169 P. Subbulakshmi and M. Prakash. Mitigating eavesdropping by using fuzzy based MDPOP-Q learning approach and multilevel Stackelberg game theoret