Random Fourier Feature Based Deep Learning for Wireless Communications

TL;DR

This paper analytically demonstrates that Random Fourier Features (RFF) enhance deep learning accuracy and reduce misclassification in wireless communication tasks, especially with limited training data, and proposes a new distribution-dependent RFF for improved performance.

Contribution

It provides the first rigorous analytical proof of RFF-based deep learning advantages and introduces a novel distribution-dependent RFF to lower training complexity in wireless communication applications.

Findings

RFF-based DL architectures have lower approximation error and misclassification probability.

The proposed distribution-dependent RFF outperforms classical RFF in various tasks.

Significant performance gains are observed with RFF maps in low training-data regimes.

Abstract

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.