A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications
Conventional multiple input multiple output (MIMO) detection algorithms face challenges related to computational complexity and limited performance when handling high-dimensional inputs and complex channel conditions. In order to enhance signal recovery accuracy in atmospheric turbulence channels fo...
Main Authors: | Minghua Cao, Ruifang Yao, Qinxue Sun, Yue Zhang, Qing Yang, Huiqin Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Photonics Journal |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10462083/ |
Similar Items
-
LSTM Attention Neural-Network-Based Signal Detection for Hybrid Modulated Faster-Than-Nyquist Optical Wireless Communications
by: Minghua Cao, et al.
Published: (2022-11-01) -
The Evolution of Faster-Than-Nyquist Signaling
by: Takumi Ishihara, et al.
Published: (2021-01-01) -
Faster-Than-Nyquist Signaling: An Overview
by: Jiancun Fan, et al.
Published: (2017-01-01) -
Low-Complexity and Highly-Robust Chromatic Dispersion Estimation for Faster-than-Nyquist Coherent Optical Systems
by: Tao Yang, et al.
Published: (2022-09-01) -
Faster-Than-Nyquist Signal Design for Multiuser Multicell Indoor Visible Light Communications
by: Yi-Jun Zhu, et al.
Published: (2016-01-01)