On the Parameter Selection of Phase-transmittance Radial Basis Function Neural Networks for Communication Systems

TL;DR

This paper introduces a novel parameter initialization method for deep phase-transmittance RBF neural networks, significantly improving convergence and performance in noisy communication environments like 5G MIMO systems.

Contribution

It proposes a new deep PT-RBF parameter initialization technique that ensures convergence and outperforms traditional methods in complex communication tasks.

Findings

Outperforms conventional initialization strategies

Achieves successful convergence in deep PT-RBF architectures

Validated through simulations conforming to 3GPP TS 38 standards

Abstract

In the ever-evolving field of digital communication systems, complex-valued neural networks (CVNNs) have become a cornerstone, delivering exceptional performance in tasks like equalization, channel estimation, beamforming, and decoding. Among the myriad of CVNN architectures, the phase-transmittance radial basis function neural network (PT-RBF) stands out, especially when operating in noisy environments such as 5G MIMO systems. Despite its capabilities, achieving convergence in multi-layered, multi-input, and multi-output PT-RBFs remains a daunting challenge. Addressing this gap, this paper presents a novel Deep PT-RBF parameter initialization technique. Through rigorous simulations conforming to 3GPP TS 38 standards, our method not only outperforms conventional initialization strategies like random, $K$-means, and constellation-based methods but is also the only approach to achieve successful convergence in deep PT-RBF architectures. These findings pave the way to more robust and efficient neural network deployments in complex digital communication systems.