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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
Non-Diffracting Beams for Near-Field Millimeter-Wave Comm...
Yifeng Qin, Jing Chen, Zhi Hao Jiang, Zhining Chen, Yongming Hua · 2025-10-17 · via eess.SP updates on arXiv.org

Near-field blockage changes the beam-design objective in millimeter-wave links: maximizing the unblocked on-axis gain does not necessarily maximize blocked-link performance. This paper studies when phase-only, aperture-constrained non-diffracting (ND) beams provide a blocked-link advantage over equal-aperture, equal-power conventional reference beams. We develop a unified annular-spectrum framework that generates isotropic Bessel-like and anisotropic Mathieu-like beams under discrete phased-array constraints, and a geometry-aware analysis centered on three propagation landmarks: the peak-intensity distance, the crossover distance, and an effective post-blockage recovery distance. Their relationship yields a recovery-before-crossover condition linking blockage size, depth, cone angle, and usable ND range, and motivates a blocked-link gain ratio that maps directly onto an achievable-rate gap at every operating SNR. The analysis also explains why anisotropic Mathieu-like beams can outperform isotropic ones under direction-dependent blockage. Monte Carlo simulations verify the predicted advantage regimes, an auxiliary comparison against a near-field focusing baseline confirms that the advantage persists against an unblocked-optimal array, and sensitivity studies over cone-angle choice and partial-transmission blockers show that the opaque-screen picture is a conservative reading of the underlying physics. The results identify Bessel-like and Mathieu-like beams as practical candidates for blockage-resilient near-field communications.