Open AccessFuture OFDM-based Communication Systems Towards 6G and Beyond: Machine Learning Approaches
Author(s) -
Filbert H. Juwono,
Regina Reine
Publication year - 2021
Publication title -
green intelligent systems and applications
Language(s) - English
Resource type - Journals
ISSN - 2809-1116
DOI - 10.53623/gisa.v1i1.34
Subject(s) - orthogonal frequency division multiplexing , computer science , multipath propagation , robustness (evolution) , communications system , frequency offset , electronic engineering , fading , data transmission , visible light communication , latency (audio) , telecommunications , computer network , engineering , channel (broadcasting) , electrical engineering , light emitting diode , biochemistry , chemistry , gene
The vision towards 6G and beyond communication systems demands higher rate transmission, massive amount of data processing, and low latency communication. Orthogonal Frequency Division Modulation (OFDM) has been adopted in the current 5G networks and has become one of the potential candidates for the future communication systems. Although OFDM offers many benefits including high spectrum efficiency and high robustness against the multipath fading channels, it has major challenges such as frequency offset and high Peak to Power Ratio (PAPR). In 5G communication network, there is a significant increase in the number of sensors and other low-power devices where users or devices may create large amount of connection and dynamic data processing. In order to deal with the increasingly complex communication network, Machine Learning (ML) has been increasingly utilised to create intelligent and more efficient communication network. This paper discusses challenges and the impacts of embedding ML in OFDM-based communication systems.