Two-stage deep learning-based hybrid precoder design for very large scale massive MIMO systems

Wireless networking is approaching a new era, which necessitates new frequency ranges and novel strategies. With recent circuit growth, communications over the Terahertz (THz) band is proving to be a viable option because of the tremendous bandwidth and low cost. On the other hand, THz signals suffe...

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Bibliographic Details
Published in:Physical communication Vol. 54; p. 101835
Main Authors: P., Jeyakumar, Ramesh, Arvind, S., Srinitha, Nishant, V.T., Gowri, P., P., Muthuchidambaranathan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2022
Subjects:
6G networks
Deep Learning
Parallel processing
Precoding
Terahertz
Parallel processing
Terahertz
Precoding
Deep Learning
6G networks
ISSN:1874-4907, 1876-3219
Online Access:Get full text
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Summary:Wireless networking is approaching a new era, which necessitates new frequency ranges and novel strategies. With recent circuit growth, communications over the Terahertz (THz) band is proving to be a viable option because of the tremendous bandwidth and low cost. On the other hand, THz signals suffer from significant direction loss, necessitating the use of precoding. In this paper, Deep Learning (DL) based precoding techniques for upcoming 6G networks were examined, along with their complexities. Based on the signal-to-noise ratio (SNR) and spectral efficiency (SE), the proposed DL-based precoding scheme is compared to traditional model-based precoding schemes. The proposed DL-based precoding technique is ideal for 6G networks, according to simulation results. Furthermore, the proposed DL-based precoding technique has lower computational complexity, making it suitable for parallel processing and high-speed data transmission.
ISSN:1874-4907
1876-3219
DOI:10.1016/j.phycom.2022.101835