Deep Complex-valued Radial Basis Function Neural Networks and Parameter Selection

TL;DR

This paper introduces a deep complex-valued radial basis function neural network architecture with a novel parameter initialization scheme, demonstrating superior convergence and performance in noisy digital communication environments like 5G MIMO systems.

Contribution

It extends shallow C-RBF networks to deep architectures and proposes a new initialization method that outperforms traditional strategies in complex-valued neural networks.

Findings

Deep C-RBF networks outperform traditional methods in noisy environments.

The proposed initialization scheme ensures successful convergence of deep C-RBF networks.

Simulations conforming to 3GPP standards validate the effectiveness of the approach.

Abstract

In the ever-evolving field of artificial neural networks and learning systems, complex-valued neural networks (CVNNs) have become a cornerstone, achieving exceptional performance in image processing and telecommunications. More precisely, in digital communication systems, CVNNs have been delivering significant results in tasks like equalization, channel estimation, beamforming, and decoding. Among the CVNN architectures, the complex-valued radial basis function neural network (C-RBF) stands out, especially when operating in noisy environments such as 5G multiple-input multiple-output (MIMO) systems. In such a context, this paper extends the classical shallow C-RBF to deep architectures, increasing its flexibility for a wider range of applications. Also, based on the parameter selection of the phase transmittance radial basis function (PT-RBF) neural network, we propose an initialization scheme for the deep C-RBF. Via rigorous simulations conforming to 3GPP TS 38 standards for digital communications, our method not only outperforms conventional initialization strategies like random, $K$-means, and constellation-based methods but it also seems to be the only approach to achieve successful convergence for deep C-RBF architectures. These findings pave the way to more robust and efficient neural network deployments in complex-valued digital communication systems.