Work

Weight-Clustered Neural Networks for Low-Complexity Nonlinear Equalization in Digital Subcarrier Multiplexing Systems

Year: 2025

Type: article

Abstract: We propose a low-complexity weight-clustered NN-based equalizer for digital subcarrier multiplexing systems. Our approach achieved a 91% complexity reduction compared to the perturbation-based model in previous literature and 93% relative to a nonclustered NN.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16778824

Authors: Srivallapanonodh, Sasipim, Freire de Carvalho Souza, Pedro Jorge, Parisi, Giuseppe, Devigili, Mariano, Costa, Nelson, Spinnler, Bernhard, Napoli, Antonio, Prilepsky, Jaroslaw, Turitsyn, Sergei

Institution: Aston University

Language: English

Topic: Advanced Wireless Communication Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: UK Research and Innovation, European Commission

Awards: Machine LEarning in Optical NeTwORks, Agile uLtra Low EnerGy secuRe netwOrks, G8465161875

OpenAlex ID: W6893316443

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