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

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...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Soares, Jonathan A, Mayer, Kayol S, Arantes, Dalton S
Format: Article
Language:English
Subjects:
Beamforming
Communications systems
Convergence
Multilayers
Neural networks
Parameters
Radial basis function
Task complexity
Transmittance
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2408.07692