@article{maraqa_rajasekaran_2020, 
  title={A Survey of Rate-optimal Power Domain NOMA with Enabling Technologies of   Future Wireless Networks}, 
  abstractNote={The ambitious high data-rate applications in the envisioned future B5G
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 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-enabled transmission 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 PD-NOMA. In this paper, we
survey the integration of PD-NOMA with the enabling communications schemes and
technologies that are expected to meet the various 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 MISO,
MIMO, mMIMO, advanced antenna architectures, mmWave and THz, CoMP, cooperative
communications, cognitive radio, VLC, UAV and others. The considered system
models, the optimization methods utilized 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 detail along with the
future research directions for these combined schemes. Moreover, the role of
machine learning in optimizing these NOMA-enabled technologies is addressed.}, 
  author={Maraqa and Rajasekaran}, 
  year={2020}, 
  month={Jul}
  }