Yadav Govind Sharan, Torkaman Pouya, Miao Xuan-Wei, Feng Kai-Ming, Yang Shang-Hua
Opt Lett. 2022 Sep 1;47(17):4431-4434. doi: 10.1364/OL.468331.
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.
在本信函中，我们通过在一个15公里的单模光纤（SMF）上传输16正交幅度调制（QAM）正交频分复用（OFDM）信号，并在135GHz下通过一个具有成本效益的强度调制直接检测（IM-DD）通信系统实现60厘米的无线链路距离，提出并通过实验验证了一种用于太赫兹室内无线光纤的稀疏深度学习方法（SDLM）。所提出的SDLM在成本函数上施加了L1正则化机制，与传统的沃尔泰拉非线性均衡器（VNLE）、稀疏VNLE和传统的DLM相比，这不仅提高了性能，还降低了复杂度。我们的实验结果表明，所提出的SDLM为成功减轻非线性失真提供了可行的选择，并且在不损失信号完整性的情况下，对于8-QAM分别比传统VNLE、传统DLM和SVNLE降低了76%、72%和61%的复杂度。
