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Found 19 papers, 2 papers with code
Variational Bayesian Learning based Joint Localization and Channel Estimation with Distance-dependent Noise
In this study, we address a joint localization and channel estimation problem encompassing distance-dependent noise, unknown channel parameters, and uncertainties in sensor node locations.
Variational Bayesian Learning Based Localization and Channel Reconstruction in RIS-aided Systems
As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction.
Semi-Passive Intelligent Reflecting Surface Enabled Sensing Systems
Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS).
A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems
Accurate channel state information (CSI) is essential for downlink precoding at the base station (BS), especially for frequency FDD wideband massive MIMO systems with OFDM.
Integrated Sensing and Communications in Clutter Environment
Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel model that includes both static environment and dynamic targets.
Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing
Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT.
Demo: Reconfigurable Distributed Antennas and Reflecting Surface (RDARS)-aided Integrated Sensing and Communication System
Integrated sensing and communication (ISAC) system has been envisioned as a promising technology to be applied in future applications requiring both communication and high-accuracy sensing.
YOLO: An Efficient Terahertz Band Integrated Sensing and Communications Scheme with Beam Squint
Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different OFDM symbols to estimate the distance and velocity of dynamic targets.
Reconfigurable Intelligent Surface-Enabled Gridless DoA Estimation System for NLoS Scenarios
The conventional direction-of-arrival (DoA) estimation approaches only be effective when the line-of-sight (LoS) link exists, while in the case of the non-line-of-sight (NLoS) situation, the spatial angle can not be captured and thus the DoA estimation performance would be significantly degraded.
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots
To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period.
Explicit Motion Disentangling for Efficient Optical Flow Estimation
In this paper, we propose a novel framework for optical flow estimation that achieves a good balance between performance and efficiency.
Joint Optimization of Active and Passive Beamforming in Multi-IRS Aided mmWave Communications
To explore the impact of IRS on the performance of mmWave communication, we investigate a multi-IRS assisted mmWave communication network and formulate a sum rate maximization problem by jointly optimizing the active and passive beamforming and the set of IRSs for assistance.
Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach
Deploying ultra-dense networks that operate on millimeter wave (mmWave) band is a promising way to address the tremendous growth on mobile data traffic.
Natural Language Processing for Smart Healthcare
Smart healthcare has achieved significant progress in recent years.
Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning
Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy.
Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers
Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems.
Convergence analysis of belief propagation for pairwise linear Gaussian models
Gaussian belief propagation (BP) has been widely used for distributed inference in large-scale networks such as the smart grid, sensor networks, and social networks, where local measurements/observations are scattered over a wide geographical area.
Convergence analysis of the information matrix in Gaussian belief propagation
Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local measurements/observations are scattered over a wide geographical area.
Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
A necessary and sufficient convergence condition for the belief mean vector to converge to the optimal centralized estimator is provided under the assumption that the message information matrix is initialized as a positive semidefinite matrix.
