Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application

• URL: http://arxiv.org/abs/2501.07030v1
• Date: Mon, 13 Jan 2025 03:02:15 GMT
• Title: Erasing Noise in Signal Detection with Diffusion Model: From Theory to Application
• Authors: Xiucheng Wang, Peilin Zheng, Nan Cheng,
• Abstract summary: A signal detection method based on the denoise diffusion model (DM) is proposed.<n>It outperforms the maximum likelihood (ML) estimation method.<n>For BPSK and QAM modulation schemes, the DM-based method achieves a significantly lower symbol error rate.
• Score: 12.770304903229292
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, a signal detection method based on the denoise diffusion model (DM) is proposed, which outperforms the maximum likelihood (ML) estimation method that has long been regarded as the optimal signal detection technique. Theoretically, a novel mathematical theory for intelligent signal detection based on stochastic differential equations (SDEs) is established in this paper, demonstrating the effectiveness of DM in reducing the additive white Gaussian noise in received signals. Moreover, a mathematical relationship between the signal-to-noise ratio (SNR) and the timestep in DM is established, revealing that for any given SNR, a corresponding optimal timestep can be identified. Furthermore, to address potential issues with out-of-distribution inputs in the DM, we employ a mathematical scaling technique that allows the trained DM to handle signal detection across a wide range of SNRs without any fine-tuning. Building on the above theoretical foundation, we propose a DM-based signal detection method, with the diffusion transformer (DiT) serving as the backbone neural network, whose computational complexity of this method is $\mathcal{O}(n^2)$. Simulation results demonstrate that, for BPSK and QAM modulation schemes, the DM-based method achieves a significantly lower symbol error rate (SER) compared to ML estimation, while maintaining a much lower computational complexity.

Related papers

• Machine learning empowered Modulation detection for OFDM-based signals [37.371849684842715]
  We propose a blind ML-based modulation detection for OFDM-based technologies. Our approach employs a ResNet network to simultaneously detect the modulation type and accurately locate the cyclic prefix. As a result, our proposed modulation classification method can be applied to any OFDM-based technology without prior knowledge of the transmitted signal.
  arXiv  Detail & Related papers  (2024-08-15T14:33:09Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• Dynamical Measure Transport and Neural PDE Solvers for Sampling [77.38204731939273]
  We tackle the task of sampling from a probability density as transporting a tractable density function to the target. We employ physics-informed neural networks (PINNs) to approximate the respective partial differential equations (PDEs) solutions. PINNs allow for simulation- and discretization-free optimization and can be trained very efficiently.
  arXiv  Detail & Related papers  (2024-07-10T17:39:50Z)
• Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications [64.20396555814513]
  This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA) We exploit abundant unlabeled data in the target domain to ensure effective prediction by establishing pairwise correspondence with equivalent signal variances between domains. MSA outperforms recent methods in brain-age regression with task variations using magnetoencephalography (MEG) signals from the Cam-CAN dataset.
  arXiv  Detail & Related papers  (2024-01-24T19:04:49Z)
• Noise in the reverse process improves the approximation capabilities of diffusion models [27.65800389807353]
  In Score based Generative Modeling (SGMs), the state-of-the-art in generative modeling, reverse processes are known to perform better than their deterministic counterparts. This paper delves into the heart of this phenomenon, comparing neural ordinary differential equations (ODEs) and neural dimension equations (SDEs) as reverse processes. We analyze the ability of neural SDEs to approximate trajectories of the Fokker-Planck equation, revealing the advantages of neurality.
  arXiv  Detail & Related papers  (2023-12-13T02:39:10Z)
• A Geometric Perspective on Diffusion Models [57.27857591493788]
  We inspect the ODE-based sampling of a popular variance-exploding SDE. We establish a theoretical relationship between the optimal ODE-based sampling and the classic mean-shift (mode-seeking) algorithm.
  arXiv  Detail & Related papers  (2023-05-31T15:33:16Z)
• A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems [11.190815358585137]
  Joint channel estimation and signal detection is crucial in frequency division multiplexing systems. Traditional algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep learning (DL) methods have been investigated, but concerns regarding computational expense and lack of validation in low-SNR settings remain.
  arXiv  Detail & Related papers  (2023-03-07T06:34:04Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• Theory inspired deep network for instantaneous-frequency extraction and signal components recovery from discrete blind-source data [1.6758573326215689]
  This paper is concerned with the inverse problem of recovering the unknown signal components, along with extraction of their frequencies. None of the existing decomposition methods and algorithms is capable of solving this inverse problem. We propose a synthesis of a deep neural network, based directly on a discrete sample set, that may be non-uniformly sampled, of the blind-source signal.
  arXiv  Detail & Related papers  (2020-01-31T18:54:00Z)
• ANN-Based Detection in MIMO-OFDM Systems with Low-Resolution ADCs [0.0]
  We propose a multi-layer artificial neural network (ANN) that is trained with the Levenberg-Marquardt algorithm for use in signal detection. We consider a blind detection scheme where data symbol estimation is carried out without knowing the channel state information at the receiver.
  arXiv  Detail & Related papers  (2020-01-31T03:38:42Z)

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.