Author(s):

Jing, Guoqing; Chen, Lizhen; Wang, Peipei; Xiong, Wenjie; Huang, Zebin; Liu, Junmin; Chen, Yu; Li, Ying; Fan, Dianyuan & Chen, Shuqing

Abstract:

“Fractional vortex beam (FVB) possessing helical phase can be applied in the shift-keying communication due to its fractional orbital angular momentum (FOAM) mode, which theoretically allows an infinite increase of the transmitted capacity. However, the discontinuity of spiral phase makes FVB more likely to be disturbed in turbulence environment, and the precise measurement of distorted FOAM modes is crucial for practical FOAM-based communication application. Here, we proposed a FOAM mode recognition method with feedforward neural network (FNN). Employing the diffraction preprocessing of a two-dimensional fork grating, the original optical features of FVBs can be extended along the far-field diffraction order, endowing FNN more feature information and saving calculation time, and enlarging the detection range to conjugate FOAM modes. The simulation results show that the 9-layer FNN can identify FOAM mode with interval of 0.1 with an accuracy of 99.1% under the turbulences of \(C^n_2=1×10^{–14}m^{–2/3}\) and Δz=10m. Furthermore, we experimentally constructed a 102-ary FOAM shift-keying communication link to transmit gray image, and the signals are successfully demodulated by the FNN model with the pixel-error-rate of 0.07160. It is anticipated that the proposed FNN-based FOAM recognition method will break the limitation of precision measurement under turbulence environment in practical FOAM applications.”

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Author(s):

Juntao Hu, Yuping Tai, Liuhao Zhu, Zixu Long, Miaomiao Tang, Hehe Li, Xinzhong Li and Yangjian Cai

Abstract:

“Recently, optical vortices (OVs) have attracted substantial attention because they can provide an additional degree of freedom, i.e., orbital angular momentum (OAM). It is well known that the fractional OV (FOV) is interpreted as a weighted superposition of a series of integer OVs containing different OAM states. However, methods for controlling the sampling interval of the OAM state decomposition and determining the selected sampling OAM state are lacking. To address this issue, in this Letter, we propose a FOV by inserting multiple fractional phase jumps into whole phase jumps (2π), termed as a multi-fractional OV (MFOV). The MFOV is a generalized FOV possessing three adjustable parameters, including the number of azimuthal phase periods (APPs), N; the number of whole phase jumps in an APP, K; and the fractional phase jump, α. The results show that the intensity and OAM of the MFOV are shaped into different polygons based on the APP number. Through OAM state decomposition and OAM entropy techniques, we find that the MFOV is constructed by sparse sampling of the OAM states, with the sampling interval equal to N. Moreover, the probability of each sampling state is determined by the parameter α, and the state order of the maximal probability is controlled by the parameter K, as K * N. This work presents a clear physical interpretation of the FOV, which deepens our understanding of the FOV and facilitates potential applications, especially for multiplexing technology in optical communication based on OAM.
This work was supported by the National Natural Science Foundation of China (NSFC) (Nos. 11974102, 11525418, 91750201, and 11974218), the Open Research Fund of State Key Laboratory of Transient Optics and Photonics, CAS (No. SKLST201901), the Innovation Group of Jinan under Grant No. 2018GXRC010, and the National key Research and Development Project of China (2019YFA0705000).”

Link to Publications Page