The nonlinear interaction of elliptically polarized counter-propagating waves in an isotropic transparent medium is analysed. Self-induced changes in the polarization of the counter-propagating light waves, are described. Using these results, a new type of modulator giving self-induced rotation of the polarization ellipse (SIRPE) is studied. The transmission of counter-propagating waves in the SIRPE modulator is shown to exhibit saturation with increase in light intensity and to be a multiple-valued function of the light intensity.The nonlinear interaction of elliptically polarized counter-propagating waves in an isotropic transparent medium is analysed. Self-induced changes in the polarization of the counter-propagating light waves, are described. Using these results, a new type of modulator giving self-induced rotation of the polarization ellipse (SIRPE) is studied. The transmission of counter-propagating waves in the SIRPE modulator is shown to exhibit saturation with increase in light intensity and to be a multiple-valued function of the light intensity.

Journal of Modern OpticsApr 1, 19882 citations

Polarization Operators

WORLD SCIENTIFIC eBooksDec 1, 20020 citations

Enhanced Belief Propagation Polar Decoder for Finite Lengths

Shajeel Iqbal, GoangSeog Choi

In this paper, we discuss the belief propagation decoding algorithm for polar codes. The performance of Polar codes for shorter lengths is not satisfactory. Motivated by this, we propose a novel technique to improve its performance at short lengths. We showed that the probability of messages passed along the factor graph of polar codes, can be increased by multiplying the current message of nodes with their previous message. This is like a feedback path in which the present signal is updated by multiplying with its previous signal. Thus the experimental results show that performance of belief propagation polar decoder can be improved using this proposed technique. Simulation results in binary-input additive white Gaussian noise channel (BI-AWGNC) show that the proposed belief propagation polar decoder can provide significant gain of 2 dB over the original belief propagation polar decoder with code rate 0.5 and code length 128 at the bit error rate (BER) of $10^{-4}$ .In this paper, we discuss the belief propagation decoding algorithm for polar codes. The performance of Polar codes for shorter lengths is not satisfactory. Motivated by this, we propose a novel technique to improve its performance at short lengths. We showed that the probability of messages passed along the factor graph of polar codes, can be increased by multiplying the current message of nodes with their previous message. This is like a feedback path in which the present signal is updated by multiplying with its previous signal. Thus the experimental results show that performance of belief propagation polar decoder can be improved using this proposed technique. Simulation results in binary-input additive white Gaussian noise channel (BI-AWGNC) show that the proposed belief propagation polar decoder can provide significant gain of 2 dB over the original belief propagation polar decoder with code rate 0.5 and code length 128 at the bit error rate (BER) of $10^{-4}$ .

Journal of the Korea Society of Digital Industry and Information ManagementSep 30, 20150 citations

Photon Polarization

Osama M. Raisuddin, Suvranu De

Springer eBooksNov 14, 20250 citations

Imaging polarization

Oliver Graydon

Nature PhotonicsApr 29, 20132 citations

Research on BP Decoding of Polar Codes Aided by Deep Learning

Chang Yun, Guiping Li

In recent years, polar codes under deep learning have become a research hotspot because of their outstanding decoding performance. The neural network-based polar codes confidence propagation decoding algorithm can improve the decoding performance significantly, but the computational complexity and delay of the neural network-based polar codes confidence propagation decoding algorithm still need to be improved. In this paper, the theory of polar codes coding and the traditional polar codes confidence propagation decoding algorithm are introduced. The polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network and the advantages and disadvantages of different decoding algorithms are mainly discussed, and the computational complexity of these algorithms is analyzed. According to the offset minimum sum approximation and scaling offset minimum sum approximation, the polar codes confidence propagation decoding algorithm based on recurrent neural network is optimized. Simulation results show that, compared with the traditional polar codes confidence propagation decoding algorithm, the decoding algorithm improves the decoding performance, reduces the computational complexity and reduces the iteration times of the algorithm. Compared with the polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network, the multiplication and memory overhead of the algorithm are greatly reduced while the decoding performance of the algorithm is guaranteed.In recent years, polar codes under deep learning have become a research hotspot because of their outstanding decoding performance. The neural network-based polar codes confidence propagation decoding algorithm can improve the decoding performance significantly, but the computational complexity and delay of the neural network-based polar codes confidence propagation decoding algorithm still need to be improved. In this paper, the theory of polar codes coding and the traditional polar codes confidence propagation decoding algorithm are introduced. The polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network and the advantages and disadvantages of different decoding algorithms are mainly discussed, and the computational complexity of these algorithms is analyzed. According to the offset minimum sum approximation and scaling offset minimum sum approximation, the polar codes confidence propagation decoding algorithm based on recurrent neural network is optimized. Simulation results show that, compared with the traditional polar codes confidence propagation decoding algorithm, the decoding algorithm improves the decoding performance, reduces the computational complexity and reduces the iteration times of the algorithm. Compared with the polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network, the multiplication and memory overhead of the algorithm are greatly reduced while the decoding performance of the algorithm is guaranteed.

International Conference on Computer Network, Electronic and Automation (ICCNEA)Sep 1, 20212 citations

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