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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
Connectionless Bluetooth Channel Sounding via PAwR for Sc...
[Submitted on 16 May 2026 (v1), last revised 4 Aug 2026 (this ve · 2026-05-17 · via eess.SP updates on arXiv.org

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Abstract:Bluetooth Core Specification v6.0 introduces Channel Sounding (CS) as a high-accuracy ranging primitive for Bluetooth Low Energy. However, the standard procedure requires per-pair connections. This binds ranging to a multi-stage initiation procedure, limits concurrent partners per radio, and forces result transfer over the connection. We present a connectionless CS architecture combining the LE CS Test command with Periodic Advertising with Responses (PAwR). A Central Orchestrator, a gateway, and synchronized CS devices handle coordination, configuration alignment, and result aggregation at the application layer. Each device derives its role, deterministic random bit generator initialization state, channel sequence, and response slot assignment from its device index and a Peer-to-Peer Assignment Matrix. The deterministic channel sequence prevents same-step collisions across parallel CS procedures, and the matrix can be updated per cycle to reconfigure arbitrary device-to-device pairings within a PAwR subevent group. A compact data plane omits fields recoverable from the shared measurement configuration and reduces the ranging-data payload by approximately 69%, so complete results are reported through PAwR response slots. A proof-of-concept evaluation on the nRF54L15 platform shows that deterministic channel management eliminates the collision-induced outliers observed under simulated dense-deployment channel overlaps. At a 1 s update cycle, the architecture reduces steady-state active charge by 40-48% relative to a fair connected baseline, cuts per-switch initiation overhead by approximately 98%, and, under per-cycle partner switching, achieves up to 88% lower total charge over 24 h. An empirical timing model projects up to 14,080 active devices per PAwR train for a four-measurement workload.

Submission history

From: Leon Schex [view email]
[v1] Sat, 16 May 2026 17:38:27 UTC (20,014 KB)
[v2] Tue, 4 Aug 2026 11:14:05 UTC (18,643 KB)