A Survey of Rate-optimal Power Domain NOMA with Enabling Technologies of
Future Wireless Networks
release_siq5waxbgfem5dqcmfbtgltkda
by
Omar Maraqa, Aditya S. Rajasekaran, Saad Al-Ahmadi,
Halim Yanikomeroglu
2020
Abstract
The ambitious high data rate applications in the envisioned future B5G
wireless networks require new solutions, including the advent of more advanced
architectures than the ones already used in 5G networks, and the coalition of
different communications schemes and technologies to enable these applications
requirements. Among the candidate communications schemes for future wireless
networks are NOMA schemes that allow serving more than one user in the same
resource block by multiplexing users in other domains than frequency or time.
In this way, NOMA schemes tend to offer several advantages over OMA schemes
such as improved user fairness and spectral efficiency, higher cell-edge
throughput, massive connectivity support, and low transmission latency. With
these merits, NOMA schemes are being increasingly looked at as promising
multiple access schemes for future wireless networks. When the power domain is
used to multiplex the users, it is referred to as the PD-NOMA. In this paper,
we survey the integration of PD-NOMA with the other enabling communication
schemes and technologies that are expected to satisfy the requirements of B5G
networks. In particular, this paper surveys the different rate optimization
scenarios studied in the literature when PD-NOMA is combined with one or more
of the candidate schemes and technologies for B5G networks including advanced
antenna architectures, mmWave and THz communications, CoMP, FD communications,
cognitive radio, VLC, UAV communications and others. The considered system
models, the optimization methods used to maximize the achievable rates, and the
main lessons learnt on the optimization and the performance of these
NOMA-enabled schemes and technologies are discussed in details along with the
future research directions for these combined schemes. Moreover, the role of
machine learning in optimizing these NOMA-enabled technologies is addressed.
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