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oe1(光电查) - 科学论文

2 条数据
?? 中文(中国)
  • Optimal Hexagonal Constellations Based on A Two-Dimensional Signal Space for Peak-Limited Intensity-Modulated Channels

    摘要: For high-rate peak-limited intensity-modulated optical systems, the two-dimensional time-disjoint signal space (TDSS) is proposed. Based on the TDSS, the optimal hexagonal constellations (OHCs) are proposed analytically under a peak optical power constraint. Both analytical and simulation results show our proposed OHCs based on TDSS have asymptotical peak optical power gain of 0.753 dB over the baseline schemes at no extra bandwidth cost.

    关键词: two-dimensional,peak-limited,IM/DD,signal space,Hexagonal lattice,AWGN

    更新于2025-09-23 15:22:29

  • [IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe, Japan (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - Deep Neural Network for Source Localization Using Underwater Horizontal Circular Array

    摘要: This paper applies deep neural network (DNN) to source localization in a shallow water environment using underwater horizontal circular array. The proposed method can discriminate source locations in a three-dimension space. The proposed method adopts a two-stage scheme, incorporating feature extraction and DNN analysis. In feature extraction step, the eigenvectors corresponding to the modal signal space, which are shown to be able to represent the propagating modes of the sound source, are extracted as the input feature of DNN. The eigenvectors are obtained by applying eigenvalue decomposition (EVD) of the covariance matrix of the received multi-channel signal. In DNN analysis step, time delay neural network (TDNN) is used to construct the mapping relationship between the eigenvectors and the source locations, because it is capable of making use of sequential information of the source signal. The output of the network is the source location estimates. Several experiments are conducted to demonstrate the effectiveness of the proposed method.

    关键词: shallow water environment,modal signal space,Deep neural network,horizontal circular array,source localization

    更新于2025-09-23 15:22:29