TY - CHAP
T1 - Artificial Intelligence (AI) Enabled NOMA
AU - Liu, Yuanwei
AU - Qin, Zhijin
AU - Ding, Zhiguo
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Recently, machine learning has been extensively applied in various areas including wireless communications. The more recent work has shown the power of deep learning in physical layer communications (Qin et al., IEEE Wireless Commun 26:93–99, 2019) and resource allocation (Ye et al., IEEE Veh Technol Mag 13:94–101, 2018). In this chapter, we will discuss the adaptive NOMA enabled by artificial intelligence AI and the new application of NOMA in the unmanned aerial vehicle (UAV) networks, with the goal to provide a potential solution to realize UAV networks with NOMA.
AB - Recently, machine learning has been extensively applied in various areas including wireless communications. The more recent work has shown the power of deep learning in physical layer communications (Qin et al., IEEE Wireless Commun 26:93–99, 2019) and resource allocation (Ye et al., IEEE Veh Technol Mag 13:94–101, 2018). In this chapter, we will discuss the adaptive NOMA enabled by artificial intelligence AI and the new application of NOMA in the unmanned aerial vehicle (UAV) networks, with the goal to provide a potential solution to realize UAV networks with NOMA.
UR - http://www.scopus.com/inward/record.url?scp=85074975473&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30975-6_6
DO - 10.1007/978-3-030-30975-6_6
M3 - Chapter
AN - SCOPUS:85074975473
T3 - SpringerBriefs in Computer Science
SP - 89
EP - 94
BT - SpringerBriefs in Computer Science
PB - Springer
ER -