Coherent light QPSK judgment method and system based on optical neural network

Abstract

The invention discloses a coherent light QPSK judgment method based on an optical neural network. The method comprises the steps that firstly, according to a QPSK judgment problem, an optical neural network ONN model structure is designed, model parameters are trained, a judgment photon circuit is set up and set up according to the parameters, and the judgment photon circuit is a 2 * 4 optical network and is provided with two input ports and four output ports; and then, the QPSK receiving signal and the local oscillator signal are input into two input ports of a judgment photon circuit respectively at the same time, then processed and output through four output ports, and the phase difference between the receiving signal and the local oscillator signal is judged according to the port with the highest output power so as to complete QPSK judgment. The judgment method can meet the requirements of an all-optical communication network and an optical mobile communication system, received signals and local oscillation signals are directly processed in an optical band, and judgment or branching of the optical signals is directly completed according to phase difference information of two paths of input signals. The invention also discloses a coherent light QPSK decision system based on the optical neural network.

Description

technical field [0001] The invention belongs to the field of judgment, and in particular relates to a coherent light QPSK judgment method and a judgment system based on an optical neural network. Background technique [0002] The homodyne detection (homodyne detection) scheme in the traditional coherent optical communication system uses a 90-degree optical mixer (90degree hybrid) and a balanced receiver (balanced receiver) to extract the I-path and Q-path information of the received signal, and pass the 90degree optical The mixer splits the received signal and the local oscillator signal, performs operations such as phase shifting and combining, and then completes photoelectric conversion and differential subtraction to extract I-channel and Q-channel modulation information through a balanced receiver. The above system can restore the modulation information well under the condition of ideal synchronization, and the balanced receiver is also beneficial to reduce the influence...

AI Technical Summary

Problems solved by technology

The ONN processing object proposed by Yichen Shen et al. is a real number vector, and the weight matrix in the neural network is a real number matrix. For the judgment of the optical signal, when dealing with the optical signal with phase information, the real number matrix cannot complete the optical signal. Universal phase shifting operation, so it needs to be adjusted for the structure of traditional ONN to meet the needs of coherent optical QPSK decision system

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