DM-SBL: Channel Estimation under Structured Interference

• URL: http://arxiv.org/abs/2412.05582v1
• Date: Sat, 07 Dec 2024 08:26:00 GMT
• Title: DM-SBL: Channel Estimation under Structured Interference
• Authors: Yifan Wang, Chengjie Yu, Jiang Zhu, Fangyong Wang, Xingbin Tu, Yan Wei, Fengzhong Qu,
• Abstract summary: 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.
• Score: 6.94612534055972
• License:
• Abstract: Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. While most works deal with a simple scenario where the measurements are corrupted by the additive white Gaussian noise (AWGN), this work addresses the more challenging scenario where both AWGN and structured interference coexist. Such conditions arise, for example, when a sonar/radar transmitter and a communication receiver operate simultaneously within the same bandwidth. 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. Firstly, the score of the structured interference is learned via a neural network based on the diffusion model (DM), while the channel prior is modeled as a Gaussian distribution, with its variance controlling channel sparsity, similar to the setup of the sparse Bayesian learning (SBL). Then, two efficient posterior sampling methods are proposed to jointly estimate the sparse channel and the interference. Nuisance parameters, such as the variance of the prior are estimated via the expectation maximization (EM) algorithm. The proposed method is termed as DM based SBL (DM-SBL). Numerical simulations demonstrate that DM-SBL significantly outperforms conventional approaches that deal with the AWGN scenario, particularly under low signal-to-interference ratio (SIR) conditions. Beyond channel estimation, DM-SBL also shows promise for addressing other linear inverse problems involving structured interference.

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)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Asymmetric Diffusion Based Channel-Adaptive Secure Wireless Semantic Communications [5.539381022630274]
  We propose a secure semantic communication system, DiffuSeC, which leverages the diffusion model and deep reinforcement learning (DRL) With the diffusing module in the sender end and the asymmetric denoising module in the receiver end, the DiffuSeC mitigates the perturbations added by semantic attacks. To further improve the robustness under unstable channel conditions caused by semantic attacks, we developed a DRL-based channel-adaptive diffusion step selection scheme.
  arXiv  Detail & Related papers  (2023-10-30T11:00:47Z)
• Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI [14.04355073946466]
  A deep autoencoder (DAE)-based structure for endto-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimize the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER) An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes.
  arXiv  Detail & Related papers  (2023-10-23T15:23:42Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals [98.95383921866096]
  We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. We show that capturing high-resolution temporal structures (nonstationarities) leads to substantial performance gains. We propose a domain-informed neural network (NN) design that is able to improve upon both "off-the-shelf" NNs and classical detection and interference rejection methods.
  arXiv  Detail & Related papers  (2022-09-11T14:10:37Z)
• Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
  We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT) The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
  arXiv  Detail & Related papers  (2022-05-22T12:16:43Z)
• Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems [13.267048706241157]
  We propose a deep generative model (DGM)-based technique to address this challenge. Exploiting the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent underlying channel parameters. Then, the frequency-specific underlying channel parameters, namely, the phase of each propagation path, are estimated via downlink training.
  arXiv  Detail & Related papers  (2022-03-09T18:32:10Z)
• Channel Parameter Estimation in the Presence of Phase Noise Based on Maximum Correntropy Criterion [6.47243430672461]
  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.
  arXiv  Detail & Related papers  (2021-12-15T08:26:07Z)
• Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)

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.