Application of Transformers for Nonlinear Channel Compensation in Optical Systems

• URL: http://arxiv.org/abs/2304.13119v3
• Date: Thu, 1 Aug 2024 15:52:32 GMT
• Title: Application of Transformers for Nonlinear Channel Compensation in Optical Systems
• Authors: Behnam Behinaein Hamgini, Hossein Najafi, Ali Bakhshali, Zhuhong Zhang,
• Abstract summary: We introduce a new nonlinear optical channel equalizer based on Transformers. By leveraging parallel computation and attending directly to the memory across a sequence of symbols, we show that Transformers can be used effectively for nonlinear compensation.
• Score: 0.23499129784547654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a new nonlinear optical channel equalizer based on Transformers. By leveraging parallel computation and attending directly to the memory across a sequence of symbols, we show that Transformers can be used effectively for nonlinear compensation (NLC) in coherent long-haul transmission systems. For this application, we present an implementation of the encoder part of the Transformer and analyze its performance over a wide range of different hyper-parameters. It is shown that by proper embeddings and processing blocks of symbols at each iteration and also carefully selecting subsets of the encoder's output to be processed together, an efficient nonlinear equalization can be achieved for different complexity constraints. To reduce the computational complexity of the attention mechanism, we further propose the use of a physic-informed mask inspired by nonlinear perturbation theory. We also compare the Transformer-NLC with digital back-propagation (DBP) under different transmission scenarios in order to demonstrate the flexibility and generalizability of the proposed data-driven solution.

Related papers

• Variable-size Symmetry-based Graph Fourier Transforms for image compression [65.7352685872625]
  We propose a new family of Symmetry-based Graph Fourier Transforms of variable sizes into a coding framework. Our proposed algorithm generates symmetric graphs on the grid by adding specific symmetrical connections between nodes. Experiments show that SBGFTs outperform the primary transforms integrated in the explicit Multiple Transform Selection.
  arXiv  Detail & Related papers  (2024-11-24T13:00:44Z)
• Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Waveguides [0.0]
  We numerically analyze a photonic unconventional accelerator based on the four-wave mixing effect in highly nonlinear waveguides. By exploiting the rich Kerr-induced nonlinearities, multiple nonlinear transformations of an input signal can be generated and used for solving complex nonlinear tasks.
  arXiv  Detail & Related papers  (2024-02-14T12:34:38Z)
• B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers [97.75725574963197]
  We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. We show that a sequence of such transformations induces a single linear transformation that faithfully summarises the full model computations. We show that the resulting explanations are of high visual quality and perform well under quantitative interpretability metrics.
  arXiv  Detail & Related papers  (2023-06-19T12:54:28Z)
• Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection [88.23337313766353]
  This work first provides a comprehensive statistical theory for transformers to perform ICL. We show that transformers can implement a broad class of standard machine learning algorithms in context. A emphsingle transformer can adaptively select different base ICL algorithms.
  arXiv  Detail & Related papers  (2023-06-07T17:59:31Z)
• RWKV: Reinventing RNNs for the Transformer Era [54.716108899349614]
  We propose a novel model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers.
  arXiv  Detail & Related papers  (2023-05-22T13:57:41Z)
• A Neural ODE Interpretation of Transformer Layers [8.839601328192957]
  Transformer layers, which use an alternating pattern of multi-head attention and multi-layer perceptron (MLP) layers, provide an effective tool for a variety of machine learning problems. We build upon this connection and propose a modification of the internal architecture of a transformer layer. Our experiments show that this simple modification improves the performance of transformer networks in multiple tasks.
  arXiv  Detail & Related papers  (2022-12-12T16:18:58Z)
• Error Correction Code Transformer [92.10654749898927]
  We propose to extend for the first time the Transformer architecture to the soft decoding of linear codes at arbitrary block lengths. We encode each channel's output dimension to high dimension for better representation of the bits information to be processed separately. The proposed approach demonstrates the extreme power and flexibility of Transformers and outperforms existing state-of-the-art neural decoders by large margins at a fraction of their time complexity.
  arXiv  Detail & Related papers  (2022-03-27T15:25:58Z)
• Symmetry-Aware Autoencoders: s-PCA and s-nlPCA [0.0]
  We introduce a novel machine learning embedding in the autoencoder, which uses spatial transformer networks and Siamese networks to account for continuous and discrete symmetries. The proposed symmetry-aware autoencoder is invariant to predetermined input transformations dictating the dynamics of the underlying physical system.
  arXiv  Detail & Related papers  (2021-11-04T14:22:19Z)
• Relative Positional Encoding for Transformers with Linear Complexity [30.48367640796256]
  relative positional encoding (RPE) was proposed as beneficial for classical Transformers. RPE is not available for the recent linear-variants of the Transformer, because it requires the explicit computation of the attention matrix. In this paper, we present precisely what is precisely what is a way to generate PE that can be used as a replacement to the classical additive (sinusoidal) PE and provably behaves like RPE.
  arXiv  Detail & Related papers  (2021-05-18T09:52:32Z)
• Sparse Quantized Spectral Clustering [85.77233010209368]
  We exploit tools from random matrix theory to make precise statements about how the eigenspectrum of a matrix changes under such nonlinear transformations. We show that very little change occurs in the informative eigenstructure even under drastic sparsification/quantization.
  arXiv  Detail & Related papers  (2020-10-03T15:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.