惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

GbyAI
GbyAI
阮一峰的网络日志
阮一峰的网络日志
G
Google Developers Blog
J
Java Code Geeks
Blog — PlanetScale
Blog — PlanetScale
大猫的无限游戏
大猫的无限游戏
云风的 BLOG
云风的 BLOG
Vercel News
Vercel News
L
LangChain Blog
Hugging Face - Blog
Hugging Face - Blog
T
The Blog of Author Tim Ferriss
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Stack Overflow Blog
Stack Overflow Blog
P
Proofpoint News Feed
腾讯CDC
博客园_首页
博客园 - 聂微东
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
M
MIT News - Artificial intelligence
WordPress大学
WordPress大学
D
DataBreaches.Net
Microsoft Security Blog
Microsoft Security Blog
有赞技术团队
有赞技术团队
博客园 - 叶小钗

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Scattering Environment Aware Joint Multi-BS Channel Estim...
Yani Chi · 2026-06-01 · via eess.SP updates on arXiv.org

Clock asynchronism between base stations (BSs) and users significantly degrades scatterer localization accuracy. To address this issue, this paper proposes a multi-BS joint channel estimation and localization scheme that exploits shared scatterer information among multiple BSs. First, channel modeling in the location domain is performed by leveraging the joint sparsity of multi-BS channels. Subsequently, a multi-BS scatterer association algorithm is developed based solely on Angle of Arrival (AoA) estimates. By utilizing the shared scatterers and the geometric relationships among the scatterers, BSs, and the user equipment (UE), coarse estimates of the UE location and timing offsets are obtained. Based on these coarse estimates of scatterer locations, UE location, and timing offsets, an expectation-maximization (EM) framework is employed. Specifically, the UE location and timing offsets are iteratively refined while jointly enabling high-precision estimation of scatterer locations and channel coefficients. Simulation results demonstrate that the proposed scheme achieves significant improvements in both channel estimation and localization accuracy compared with baseline methods.