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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
Ultra-low-power Monostatic Backscatter Platform with Phas...
Hanyeol Ryu, Sangkil Kim · 2026-01-05 · via eess.SP updates on arXiv.org

This paper presents a novel channel-estimation (CE) method that mitigates residual phase drifts in backscatter links and a full hardware and signal-processing pipeline for a single-antenna monostatic system. The platform comprises a semi-passive tag, a software-defined radio (SDR) reader, and a 2x1 planar Yagi-Uda array (7 dBi with higher than 30 dB isolation) operating at 2.4 ~ 2.5 GHz. The developed backscatter fading model accounts for round-trip propagation and temporal correlation, and employs an analytically derived resource-optimal pilot allocation strategy. At the receiver, optimized least square (LS) and linear minimum mean square error (LMMSE) CE with pilot-aided carrier frequency offset (CFO) compensation feed a zero-forcing (ZF) equalizer to suppress ISI. The prototype delivers 500 kbps at 1 m with power of 158 uW (SDR baseband) and 10 uW (RF switch), yielding 320 pJ/bit. OOK and BPSK modulations achieve measured EVMs of 2.97 % and 4.02 %, respectively. Performance is validated by BER measurements and successful reconstruction of a full-color image in an over-the-air experiment. The results demonstrate an ultra-low-power, multimedia-capable backscatter IoT link and provide practical hardware-software co-design guidance for scalable deployments.