Acknowledgement
이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임[No. 2018-0-00218, 초고주파 이동통신 무선백홀 전문연구실].
References
- 3GPP TR 37.817 V1.2.0, E-UTRA and NR; Study on Enhancement for Data Collection for NR and EN-DC(Release17), 2022. 1.
- 3GPP TR 23.791 V16.2.0, Study of Enablers for Network Automation for 5G, 2019. 6.
- 3GPP TS 23.288 V17.3.0, Architecture Enhancements for 5G System(5GS) to Support Network Data Analytics Services(Release 17), 2021. 12.
- 3GPP RWS-210153, NR Network Energy Saving Enhancement for Rel-18, TSG RAN Release 18 Workshop, June 28-July 2, 2021.
- 3GPP RP-193255, New WID on Enhancement of Data Collection for SON/MDT in NR, TSG RAN#86, Sitges, Barcelona, Dec. 9-12, 2019.
- 3GPP RP-201304, New SID: Study on Further Enhancement for Data Collection, TSG RAN#88e, June 29-July 3, 2020.
- 3GPP TR 37.817 V1.2.0, Study on Enhancement for Data Collection for NR and EN-DC(Release 17), 2022. 1.
- 3GPP RP-213554, New SI: Study on Network Energy Savings for NR, TSG RAN#94e, Dec. 6-17, 2021.
- 3GPP RP-213599, New SI: Study on Artificial Intelligence(AI)/Machine Learning(ML) for NR Air Interface, TSG RAN#94e, Dec. 6-17, 2021.
- 3GPP RP-213387, Support of Artificial Intelligence Applications for 5G Advanced, TSG RAN#94e, Dec. 6-17, 2021.
- 3GPP RWS-210481, Enhancements on Predictable Mobility for Beam Management, TSG RAN WG Meeting REL-18 Workshop, June 28-July 2, 2021.
- S. Dorner et al., "Deep learning based communication over the air," IEEE J. Sel. Top. Signal Process., vol. 12, no. 1, Feb. 2018, pp. 132-143. https://doi.org/10.1109/jstsp.2017.2784180