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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
Near-Field Integrated Sensing and Communication for Multi...
Hang Ruan, Homa Nikbakht, Ruizhi Zhang, Honglei Chen, Yonina C. · 2025-06-08 · via eess.SP updates on arXiv.org

Integrated sensing and communication (ISAC) in the near-field regime offers the potential to jointly support high-rate downlink transmission and high-resolution multi-target detection by exploiting the spherical-wave nature of electromagnetic propagation. In this paper, we propose a unified beamforming framework for a multi-user multi-target near-field ISAC system. In this system, a multi-antenna base station simultaneously serves multiple single-antenna users and senses multiple point-targets without prior knowledge of their radar cross sections. By optimizing the transmit covariance matrix, our design maximizes the minimum weighted transmit beampattern gain across all targets to ensure accurate sensing while strictly limiting inter-target cross-correlations and guaranteeing per-user communication rate and total power constraints. We extend classical far-field beampattern and cross-correlation measures to the near-field by incorporating both angle and range dependencies, enabling discrimination of targets along the same direction but at different distances. The resulting non-convex program is efficiently relaxed to a semidefinite program via rank-one lifting. We then develop a closed-form reconstruction to recover optimal rank-one beamformers. Numerical simulations demonstrate that our near-field ISAC design can simultaneously resolve and serve users/targets along the same direction but at different distances, achieving significant gains over far-field and single-target benchmarks.