Improvement of SPGD by Gradient Descent Optimization Algorithm in Deep Learning

被引：0
|
作者：
Zhao, Qingsong [1 ]
Hao, Shiqi [1 ]
Wang, Yong [1 ]
Wang, Lei [1 ]
Lin, Zhi [1 ]
机构：
[1] Natl Univ Def Technol, AnHui Prov Key Lab Elect Restrict, Coll Elect Countermeasure, Hefei, Peoples R China
来源：
2022 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE, ACP | 2022年
关键词：
wavefront correction; iterative algorithm; atmospheric turbulence; ORBITAL ANGULAR-MOMENTUM; BEAM;
D O I：
10.1109/ACP55869.2022.10088667
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The distortion caused by atmospheric turbulence can be compensated by wavefront correction, which improves the performance of free space optical vortex beam communication. In order to solve the slow convergence problem of the traditional stochastic parallel gradient descent algorithm in wavefront correction, an iterative correction algorithm based on adaptive gain factor is presented, which is combined with the Adam optimization algorithm in deep learning. The simulation verifies that the algorithm is more robust in turbulent environment.
引用
收藏
页码：469 / 472
页数：4
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