Effect of ADC parameters on neural network based chaotic optical communication

Area of Science:

• Optical Communications
• Signal Processing
• Artificial Intelligence

Background:

• Traditional chaotic optical communication systems require precise parameter matching between transmitters and receivers.
• Neural network (NN)-based receivers offer a solution to this parameter matching dependency.
• Analog-to-digital converters (ADCs) introduce limitations in NN-based chaotic optical decryption performance.

Purpose of the Study:

• To numerically investigate the impact of ADC sampling rate and bit digitalization on NN-based chaotic optical communication systems.
• To analyze these effects across varying feedback strengths and bit rates.
• To provide guidance for developing high-speed NN-based chaotic optical communication systems.

Main Methods:

• Numerical simulation of NN-based chaotic optical communication systems.
• Systematic variation of ADC parameters (sampling rate, bit depth).
• Analysis of system performance under different feedback strengths and bit rates.

Main Results:

• Higher feedback strengths in chaotic optical systems necessitate improved ADC performance for effective decryption.
• Achieving higher bit rates in chaotic optical communications requires ADCs with enhanced sampling rates and bit resolution.
• The performance of the ADC is a critical factor limiting the speed and reliability of NN-based chaotic optical communication.

Conclusions:

• The study highlights the significant role of ADC specifications in the practical implementation of NN-based high-speed chaotic optical communication.
• Optimizing ADC parameters is essential for overcoming performance bottlenecks in these advanced communication systems.
• These findings offer valuable insights for the design and application of future high-speed chaotic optical communication technologies.