Title
6G Wireless Communications Networks: A Comprehensive Survey
Abstract
The commercial fifth-generation (5G) wireless communications networks have already been deployed with the aim of providing high data rates. However, the rapid growth in the number of smart devices and the emergence of the Internet of Everything (IoE) applications, which require an ultra-reliable and low-latency communication, will result in a substantial burden on the 5G wireless networks. As such, the data rate that could be supplied by 5G networks will unlikely sustain the enormous ongoing data traffic explosion. This has motivated research into continuing to advance the existing wireless networks toward the future generation of cellular systems, known as sixth generation (6G). Therefore, it is essential to provide a prospective vision of the 6G and the key enabling technologies for realizing future networks. To this end, this paper presents a comprehensive review/survey of the future evolution of 6G networks. Specifically, the objective of the paper is to provide a comprehensive review/survey about the key enabling technologies for 6G networks, which include a discussion about the main operation principles of each technology, envisioned potential applications, current state-of-the-art research, and the related technical challenges. Overall, this paper provides useful information for industries and academic researchers and discusses the potentials for opening up new research directions.
Year
DOI
Venue
2021
10.1109/ACCESS.2021.3124812
IEEE ACCESS
Keywords
DocType
Volume
6G mobile communication, 5G mobile communication, Wireless communication, Long Term Evolution, Broadband communication, Multiaccess communication, Reliability, 6G, intelligent reflecting surfaces, orthogonal multiple access, NOMA, rate-splitting multiple access, spatial modulation, cell-free massive MIMO, mmWave, terahertz (THz), holographic radio, full duplex, energy harvesting, backscatter, edge computing, optical wireless communications, blockchain, artificial intelligence, machine learning
Journal
9
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
10