Random Fourier Feature Based Deep Learning for Wireless Communications

This paper provides analytical results on fixed kernel width based RFF based DL (RFF-DL). The derived analysis and the presented case-studies indicate the RFF-DL's robustness to kernel-width initializations, and offers improved convergence in the low-data regime.