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.