Weight-Clustered Neural Networks for Low-Complexity Nonlinear Equalization in Digital Subcarrier Multiplexing Systems
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.
Institutions
- Aston University (GB)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-04-03
- DOI
- https://doi.org/10.5281/zenodo.16778824
- Primary Topic
- PAPR reduction in OFDM
- Type
- article
- Field-Weighted Citation Impact
- 0.00