Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalization

Area of Science:

• Optoelectronics
• Computational neuroscience
• Signal processing

Background:

• Reservoir computing (RC) is a bioinspired computing paradigm effective for time-dependent signal processing.
• Analog RC implementations show performance comparable to state-of-the-art algorithms but are often limited by offline training.
• Wireless communications face increasing demand for fast analog devices to handle nonlinear distorted channels.

Purpose of the Study:

• To investigate the online learning approach for optoelectronic reservoir computing.
• To apply this system to wireless communication channel equalization.
• To demonstrate improved performance over existing methods.

Main Methods:

• An optoelectronic reservoir computer was trained using an online gradient descent algorithm.
• The algorithm was implemented on a field-programmable gate array (FPGA) chip.
• The system was tested on wireless communication channel equalization tasks, including drifting and switching channels.

Main Results:

• The optoelectronic reservoir computer achieved error rates up to two orders of magnitude lower than previous implementations for channel equalization.
• The system demonstrated robust performance on realistic drifting and switching wireless communication channels.
• Online training enabled efficient adaptation to dynamic channel conditions.

Conclusions:

• Online learning significantly enhances the applicability of optoelectronic reservoir computing for real-world tasks.
• This approach offers a promising solution for fast and efficient channel equalization in wireless communications.
• The developed system is well-suited for dynamic and complex communication environments.