.png){kind=icon}
返回
Performance and complexity analysis using a sparse deep learning method for indoor terahertz transmission
.png){kind=icon}
PRE
AI
DOI：10.1364/OL.468331.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this Letter, we propose and experimentally validate a sparse deep learning method (SDLM) for terahertz indoor wireless-over-fiber by transmitting a 16-quadrature amplitude modulation (QAM) orthogonal frequency-division multiplexing (OFDM) signal over a 15-km single-mode fiber (SMF) and a wireless link distance of 60 cm at 135 GHz through a cost-effective intensity-modulated direct detection (IM-DD) communications system. The proposed SDLM imposes the L1-regularized mechanism on the cost function, which not only improves performance but also reduces complexity when compared with traditional Volterra nonlinear equalizer (VNLE), sparse VNLE, and conventional DLM. Our experimental findings show that the proposed SDLM provides viable options for successfully mitigating nonlinear distortions and outperforms conventional VNLE, conventional DLM, and SVNLE with a 76%, 72%, and 61% complexity reduction, respectively, for 8-QAM without losing signal integrity. (c) 2022 Optica Publishing Group
期刊
IF:
3.3
论文数:
4.0W
被引数:
7.6W
机构
引用论文
Nonlinearity-aware optoelectronic terahertz discrete multitone signal transmission with a zero-bias diode.png)
OPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
分享收藏Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networksOPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏
查看更多内容