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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
Enabling 6G Wireless Communications: UWB Characterization...
Juan E. Galeote-Cazorla, Alejandro Ramírez-Arroyo, Mauricio Rodr · 2026-03-19 · via eess.SP updates on arXiv.org

Future sixth-generation of wireless system is expected to provide data-rates in the order of 1 Tbps and latencies below 1 ms. Among others, one of the most promising strategies to meet these requirements is to operate at higher frequencies than millimeter wave bands: the THz bands. Nevertheless, because of the higher losses and the detriment of classical propagation mechanisms, deploying systems operating at these frequencies becomes a real challenge. Consequently, short-range scenarios are of special interest since these effects of THz bands can be managed. This work conducts an extensive campaign within corridors at frequencies within the H-band in the range from 250 GHz to 330 GHz. For the first time in literature, an ultra wideband of 80 GHz is studied simultaneously. Large scale effects are assessed by estimating and modeling path gain. The path gain exponent varies between -2.1 and -1.6, which is explained by a guiding effect also observed at millimeter wave bands. Small scale effects are also evaluated in terms of parameters such as rice $K$-factor, root mean squared delay spread and coherence bandwidth. Additionally, an analytical approximation based on the classical N-rays model is proposed obtaining an accurate representation of the wireless channel which is coherent with the empirical analysis. The full analysis reveals the suitability of these THz bands for deploying point-to-point links due to the predominance of the line-of-sight contribution respect to the reflected components.