Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                

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

Released as a article .

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.
In text/plain format

Archived Files and Locations

application/pdf  4.7 MB
file_mir7k3gy2ngwfb257dc6jb7g2m
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2020-02-21
Version   v2
Language   en ?
arXiv  1909.08011v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 2ce21c7d-53b8-4cc2-ace4-c895db9fe66f
API URL: JSON