  Deep-learning (DL) has emerged as a powerful machine-learning technique for
several classic problems encountered in generic wireless communications.
Specifically, random Fourier Features (RFF) based deep-learning has emerged as
an attractive solution for several machine-learning problems; yet there is a
lacuna of rigorous results to justify the viability of RFF based DL-algorithms
in general. To address this gap, we attempt to analytically quantify the
viability of RFF based DL. Precisely, in this paper, analytical proofs are
presented demonstrating that RFF based DL architectures have lower
approximation-error and probability of misclassification as compared to
classical DL architectures. In addition, a new distribution-dependent RFF is
proposed to facilitate DL architectures with low training-complexity. Through
computer simulations, the practical application of the presented analytical
results and the proposed distribution-dependent RFF, are depicted for various
machine-learning problems encountered in next-generation communication systems
such as: a) line of sight (LOS)/non-line of sight (NLOS) classification, and b)
message-passing based detection of low-density parity check codes (LDPC) codes
over nonlinear visible light communication (VLC) channels. Especially in the
low training-data regime, the presented simulations show that significant
performance gains are achieved when utilizing RFF maps of observations. Lastly,
in all the presented simulations, it is observed that the proposed
distribution-dependent RFFs significantly outperform RFFs, which make them
useful for potential machine-learning/DL based applications in the context of
next-generation communication systems.
