cement learning approach. IEEE Commu- nications Letters, 22(5):998-1001, 2018. 115 M. Liu, T. Song, J. Hu, J. Yang, and G. Gui. Deep learning-inspired message pass- ing algorithm for efficient resource allocation in cognitive radio networks. IEEE Transactions on Vehicular Technology, 68(1):641-653, 2019. 116 S. Liu, Y. Xu, X. Chen, X. Wang, M. Wang, W. Li, Y. Li, and Y. Xu. Pattern-aware intelligent anti-jamming communication: A sequential deep reinforcement learn- ing approach. IEEE Access, 7:169204-169216, 2019. 117 F. Liu, Y. Cui, C. Masouros, J. Xu, T. X. Han, Y. C. Eldar, and S. Buzzi. Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond. IEEE Journal on Selected Areas in Communications, 40(6):1728-1767, 2022. 118 X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han. Wireless networks with RF energy harvesting: A contemporary survey. IEEE Communications Surveys & Tutorials, 17(2):757-789, 2014. 119 L. Lv, Q. Ni, Z. Ding, and J. Chen. Application of non-orthogonal multiple access in cooperative spectrum-sharing networks over nakagami- m fading channels. IEEE Transactions on Vehicular Technology, 66(6):5506-5511, 2017. doi: 10.1109/ TVT.2016.2627559. 120 S. Ma, Y. Zhu, G. Wang, and R. He. Machine learning aided channel estimation for ambient backscatter communication systems. In 2018 IEEE International Conference on Communication Systems (ICCS), pages 67-71, 2018. 121 C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. S. Quek, and H. V. Poor. On safeguarding privacy and security in the framework of federated learning. IEEE Network, 34(4):242-248, 2020. doi: 10.1109/MNET.001.1900506. Bibliography 122 X. Ma, H. Sun, and R. Q. Hu. Scheduling policy and power allocation for federated learning in NOMA based MEC. In GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, pages 1-7, 2020. doi: 10.1109/GLOBECOM42002.2020.9322270. 123 S. Machuzak and S. K. Jayaweera. Reinforcement learning based anti-jamming with wideband autonomous cognitive radios. In 2016 IE