Machine learning (ML) techniques have gained significant attention in the field of cognitive radio networks (CRNs) due to their ability to learn and adapt to changing environments. In CRNs, ML algorithms can be used for various tasks such as spectrum sensing, spectrum allocation, power control, and cognitive routing. This literature survey provides an overview of the state-of-the-art machine learning approaches for CRNs, including reinforcement learning, deep learning, decision trees, and genetic algorithms. The potential applications of these approaches, as well as the challenges and opportunities for future research, are also discussed. The survey can serve as a valuable resource for researchers and practitioners interested in applying machine learning in CRNs.
Keywords
CRN, ML Algorithms, Machine Learning Approaches, Reinforcement Learning, Industry Case Study
Bkassiny, Mario, Yang Li, and Sudharman K. Jayaweera. "A survey on machine-learning techniques in cognitive radios." IEEE Communications Surveys & Tutorials 15, no. 3 (2012): 1136-1159.
Kaur, Amandeep, and Krishan Kumar. "A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks." Journal of Experimental & Theoretical Artificial Intelligence 34, no. 1 (2022): 1-40.
Axell, Erik, Geert Leus, Erik G. Larsson, and H. Vincent Poor. "Spectrum sensing for cognitive radio: State-of-the-art and recent advances." IEEE signal processing magazine 29, no. 3 (2012): 101-116.
Uprety, Aashma, and Danda B. Rawat. "Reinforcement learning for iot security: A comprehensive survey." IEEE Internet of Things Journal 8, no. 11 (2020): 8693-8706.
Bala, Indu, Kiran Ahuja, Komal Arora, and Danvir Mandal. "A comprehensive survey on heterogeneous cognitive radio networks." Comprehensive Guide to Heterogeneous Networks (2023): 149-178.
Naeem, Ayesha, Mubashir Husain Rehmani, Yasir Saleem, Imran Rashid, and Noel Crespi. "Network coding in cognitive radio networks: A comprehensive survey." IEEE Communications Surveys & Tutorials 19, no. 3 (2017): 1945-1973.
Ul Hassan, Muneeb, Mubashir Husain Rehmani, Maaz Rehan, and Jinjun Chen. "Differential privacy in cognitive radio networks: A comprehensive survey." Cognitive Computation 14, no. 2 (2022): 475-510.
Amjad, Muhammad, Mubashir Husain Rehmani, and Shiwen Mao. "Wireless multimedia cognitive radio networks: A comprehensive survey." IEEE Communications Surveys & Tutorials 20, no. 2 (2018): 1056-1103.
Shuja, Junaid, Kashif Bilal, Waleed Alasmary, Hassan Sinky, and Eisa Alanazi. "Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey." Journal of Network and Computer Applications 181 (2021): 103005.
Arjoune, Youness, and Naima Kaabouch. "A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions." Sensors 19, no. 1 (2019): 126.
Jalil, Syed Qaisar, Stephan Chalup, and Mubashir Husain Rehmani. "Cognitive radio spectrum sensing and prediction using deep reinforcement learning." In 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. IEEE, 2021.
Liu, Yiming, F. Richard Yu, Xi Li, Hong Ji, and Victor CM Leung. "Blockchain and machine learning for communications and networking systems." IEEE Communications Surveys & Tutorials 22, no. 2 (2020): 1392-1431.
Naparstek, Oshri, and Kobi Cohen. "Deep multi-user reinforcement learning for distributed dynamic spectrum access." IEEE transactions on wireless communications 18, no. 1 (2018): 310-323.
Huang, Xin-Lin, Xiao-Wei Tang, and Fei Hu. "Dynamic spectrum access for multimedia transmission over multi-user, multi-channel cognitive radio networks." IEEE Transactions on Multimedia 22, no. 1 (2019): 201-214.
A. Haldorai and U. Kandaswamy, “Supervised Machine Learning Techniques in Intelligent Network Handovers,” EAI/Springer Innovations in Communication and Computing, pp. 135–154, 2019. doi:10.1007/978-3-030-15416-5_7
Anandakumar H, Umamaheswari K, “Intelligent Spectrum Handovers in Cognitive Radio Networks”, Springer Innovations in Communication and Computing, ISBN 978-3-030- 15415-8, DOI 10.1007/978-3-030-15416-5.
Cite this article
Niranjani V, Premkumar Duraisamy, M. Priyadharshan, B. Gayathri, “Advancements in Machine Learning Techniques for Optimizing Cognitive Radio Networks: A Comprehensive Review”, Advances in Computational Intelligence in Materials Science, pp. 130-135, May. 2023. doi:10.53759/acims/978-9914-9946-9-8_20
