Year: 2024
Type: article
Abstract: Interference among wireless access points (APs) in Wi‐Fi systems limits the throughput of multi‐AP massive multiple‐input multiple‐output systems, and as the AP density increases, the increased interference leads to a significant loss of spectral efficiency of the system. Suppose interference is suppressed by obtaining information about all interfering channels, although the spectral efficiency of the system is greatly improved. In that case, the communication overhead between APs is too huge and consumes too many resources for coordinated transmission, and the performance improvement obtained is negligible. Based on this, a new deep learning hybrid precoding technique based on local channel information is proposed in this paper, where APs use local channel state information for direct hybrid precoding, which can effectively suppress inter‐AP interference in dense wireless local area network and improve the reachable rate of the system through the characteristics of deep learning networks. Through multi‐AP system‐level simulations, it is demonstrated that this non‐collaborative hybrid precoding method based on deep learning greatly suppresses interference and effectively improves the spectral efficiency of the system.
Source: IET Communications
DOI: 10.1049/cmu2.12847
Authors: Gang Xie, Zhixiang Pei, Gaole Long, Yuanan Liu
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,420
Award: G8194511106
OpenAlex ID: W4404143205