Channel Parameter Estimation in the Presence of Phase Noise Based on Maximum Correntropy Criterion

• URL: http://arxiv.org/abs/2112.07955v1
• Date: Wed, 15 Dec 2021 08:26:07 GMT
• Title: Channel Parameter Estimation in the Presence of Phase Noise Based on Maximum Correntropy Criterion
• Authors: Amir Alizadeh and Ghosheh Abed Hodtani
• Abstract summary: AWGN channel is considered, where the sent signal accompanying with phase noise is added to the channel Gaussian noise. We analyze this phase noise channel estimation with information theoretic learning (ITL) criterion. We improve the convergence rate by combining MSE and MCC as a novel mixed-LMS algorithm.
• Score: 6.47243430672461
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Oscillator output generally has phase noise causing the output power spectral density (PSD) to disperse around a Dirac delta function. In this paper, the AWGN channel is considered, where the sent signal accompanying with phase noise is added to the channel Gaussian noise and received at the receiver. Conventional channel estimation algorithms such as least mean square (LMS) and mean MSE criterion are not suitable for this channel estimation. We (i) analyze this phase noise channel estimation with information theoretic learning (ITL) criterion, i.e., maximum correntropy criterion (MCC), leading to robustness in the channel estimator's steady state behavior; and (ii) improve the convergence rate by combining MSE and MCC as a novel mixed-LMS algorithm.

Related papers

• Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios [3.674863913115431]
  This paper proposes a novel joint channel-estimation and source-detection algorithm using successive interference cancellation (SIC)-aided generative score-based diffusion models. The proposed algorithm outperforms existing methods in joint source-channel estimation, especially in low-rank scenarios.
  arXiv  Detail & Related papers  (2025-01-20T02:29:34Z)
• DM-SBL: Channel Estimation under Structured Interference [6.94612534055972]
  Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. This work addresses the more challenging scenario where both white Gaussian noise (AWGN) and structured interference coexist. To ensure accurate channel estimation in these scenarios, the sparsity of the channel in the delay domain and the complicate structure of the interference are jointly exploited.
  arXiv  Detail & Related papers  (2024-12-07T08:26:00Z)
• Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments [93.30657979626858]
  We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion. We show that, when channels aretemporally correlated, the inclusion of the SNR in the state representation with function approximation in ways that inhibit convergence.
  arXiv  Detail & Related papers  (2022-11-02T22:07:36Z)
• High-Order Qubit Dephasing at Sweet Spots by Non-Gaussian Fluctuators: Symmetry Breaking and Floquet Protection [55.41644538483948]
  We study the qubit dephasing caused by the non-Gaussian fluctuators. We predict a symmetry-breaking effect that is unique to the non-Gaussian noise.
  arXiv  Detail & Related papers  (2022-06-06T18:02:38Z)
• Channel Estimation and Secret Key Rate Analysis of MIMO Terahertz Quantum Key Distribution [14.156975741478018]
  We study the secret key rate (SKR) of a multiple-input multiple-output (MIMO) continuous variable quantum key distribution (CVQKD) system operating at terahertz (THz) frequencies.
  arXiv  Detail & Related papers  (2021-10-08T11:09:35Z)
• Channel-noise tracking for sub-shot-noise-limited receivers with neural networks [0.0]
  We investigate the use of a deep neural network as a computationally efficient estimator of phase and amplitude channel noise. We find that this noise tracking method allows the non-Gaussian receiver to maintain its benefit over the quantum-noise limit.
  arXiv  Detail & Related papers  (2021-02-15T16:50:01Z)
• Plug-And-Play Learned Gaussian-mixture Approximate Message Passing [71.74028918819046]
  We propose a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
  arXiv  Detail & Related papers  (2020-11-18T16:40:45Z)
• Shape Matters: Understanding the Implicit Bias of the Noise Covariance [76.54300276636982]
  Noise in gradient descent provides a crucial implicit regularization effect for training over parameterized models. We show that parameter-dependent noise -- induced by mini-batches or label perturbation -- is far more effective than Gaussian noise. Our analysis reveals that parameter-dependent noise introduces a bias towards local minima with smaller noise variance, whereas spherical Gaussian noise does not.
  arXiv  Detail & Related papers  (2020-06-15T18:31:02Z)
• Noisy receivers for quantum illumination [0.0]
  Quantum illumination (QI) promises unprecedented performances in target detection. Signal and idler recombination forms a crucial barrier to the protocol's success. This could potentially be mitigated if performing a measurement on the idler mode could still yield a quantum advantage.
  arXiv  Detail & Related papers  (2020-06-08T17:42:19Z)
• Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces [99.34306447202546]
  This paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems. We first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. The complete channel matrix can be reconstructed from the limited measurements based on compressive sensing.
  arXiv  Detail & Related papers  (2020-06-03T12:18:57Z)
• Residual-driven Fuzzy C-Means Clustering for Image Segmentation [152.609322951917]
  We elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation. Built on this framework, we present a weighted $ell_2$-norm fidelity term by weighting mixed noise distribution. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over existing FCM-related algorithms.
  arXiv  Detail & Related papers  (2020-04-15T15:46:09Z)

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.