Dynamic Computing Power Wavelength Allocation Based on Deep Q-Learning for WDM-PON

This paper proposes a dynamic computing power wavelength allocation scheme based on deep Q-learning for WDM-PON, which can reduce spectrum fragmentation during massive computing power requests. Without introducing delay, its performance in reducing blocking rate is improved by approximately 30% compared to traditional algorithms.