Hao Wen, Yang Cao, Yuchao Dang. Research on DNN-NOMS decoding method of polarization code in wireless optical communication[J]. Infrared and Laser Engineering, 2022, 51(5): 20210420
- Infrared and Laser Engineering
- Vol. 51, Issue 5, 20210420 (2022)
Fig. 1. DNN neural network model assisted system model for polarization code decoding
Fig. 2. (8，4) factor diagram of polarization code BP decoding method
Fig. 3. (8，4) PE information transmission process of the processing unit of the polarization code
Fig. 4. (8，4) Dense Tanner graph (a) and sparse Tanner graph (b) of polarization codes
Fig. 5. (8，4) sparse neural network decoding structure of polarization codes
Fig. 6. Structure diagram of DNN-NOMS neural network decoder
Fig. 7. Comparison of the decoding performance of the neural network model under different network layers
Fig. 8. Evolution of loss function and training parameters
Fig. 9. Performance comparison of different BP decoding methods
Fig. 10. Performance comparison of decoding methods under different code rates
Fig. 11. Performance comparison of decoding methods under different turbulence intensities
|
Table 1. Symbol meaning of NOMS decoding method
|
Table 2. Simulation parameters
|
Table 3. Calculation of optimal factor parameters under different turbulence intensity conditions
|
Table 4. Comparison of decoding complexity of different BP modified decoding methods
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20