Q. -V. Pham, N. T. Nguyen, T. Huynh-The, L. Bao Le, K. Lee and W. -J. Hwang, "Intelligent Radio Signal Processing: A Survey," in IEEE Access, vol. 9, pp. 83818-83850, 2021, doi: 10.1109/ACCESS.2021.3087136
Intelligent radio signal processing : a survey
|Author:||Pham, Quoc-Viet1; Nguyen, Nhan Thanh2; Huynh-The, Thien3;|
1Korean Southeast Center for the 4th Industrial Revolution Leader Education, Pusan National University, Busan 46241, South Korea
2Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
3ICT Convergence Research Center, Kumoh National Institute of Technology, Gumi 39177, South Korea
4Institut National de la Recherche Scientifique (INRS), University of Quebec, Montreal, QC H5A 1K6, Canada
5Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, South Korea
6Department of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, South Korea
|Online Access:||PDF Full Text (PDF, 4.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021090745286
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-09-07
Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.
|Pages:||83818 - 83850|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the National Research Foundation of Korea (NRF) through the Korean Government (MSIT) under Grant NRF-2019R1C1C1006143, Grant NRF-2019R1I1A3A01060518, and Grant NRF-2019R1F1A1061934; in part by the Pusan national University Research under Grant 2020; in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) through the Ministry of Science and ICT (MSIT), Korea Government, under Grant 2020-0-01450; in part by the Artificial Intelligence Convergence Research Center, Pusan National University; and in part by the MSIT, Korea, under the Grand Information Technology Research Center Support Program supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP) under Grant IITP-2021-2016-0-00318.
© The Authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.