Random Fourier Feature-Based Deep Learning for Wireless Communications

Deep-learning (DL) has emerged as a powerful machine-learning technique for several problems encountered in generic wireless communications. Also, random Fourier Features (RFF) based DL has emerged as an attractive solution for several machine-learning problems; yet existing works lack rigorous analytical results to justify the viability of RFF based DL. To address this gap, we analytically quantify the viability of RFF based DL in this paper. Precisely, we present analytical proofs which show that the RFF based DL architectures have lower approximation-error and a lower probability of misclassification as compared to classical DL architectures for a fixed dataset-size. In addition, a new distribution-dependent RFF (DDRFF) is proposed to facilitate DL architectures with low training-complexity. The presented analytical contributions and the DDRFF are validated by relevant case-studies such as: a) line of sight (LOS)/non-line of sight (NLOS) classification, and b) message-passing based detection of low-density parity check (LDPC) codes over nonlinear visible light communication (VLC) channels. Especially, in the low training-data regime, the presented simulations depict significant performance gains for RFF based DL. Lastly, in all the presented simulations, it is observed that the proposed DDRFFs significantly outperform RFFs, which make them useful for potential machine-learning/DL applications for communication systems.