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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
Long-Range Backscatter: A Bottom-Up Approach
Tijl Schepens, Gilles Callebaut, Liesbet Van der Perre · 2026-05-12 · via eess.SP updates on arXiv.org

Continued progress towards energy-neutral Internet of Things (IoT) nodes expose the wireless communication link as the dominant energy bottleneck. While low-power wide-area network (LPWAN) technologies achieve long-range communication with multiple years of battery life, their active radios hinder reaching full energy neutrality. Long-range backscatter communication emerged as a key enabler, reaching one to three order of magnitude lower power consumption. New advancements leverage concepts from active radio systems such as chirp spread spectrum (CSS) modulation and integrate them on a low-power backscatter tag. This paper presents a comprehensive survey of long-range backscatter communication, using a bottom-up analysis spanning system topologies, hardware architecture, modulation techniques and medium access. Backscatter communication requires different topologies compared to active radios to reach longer communication distances. Different hardware architectures support backscattering a modulated signal with differing complexity, power consumption and spectral efficiency. At the physical layer binary switch-based modulation are well known and provide an easy form of modulation while chirp spread spectrum (CSS)-based modulation gain traction due to their robustness. Medium Access Control (MAC) techniques are examined with a focus on synchronization, concurrency and lightweight feedback mechanisms requiring low-power, low-complexity hardware. Building on these established solutions the paper evaluates the feasibility of long-range backscatter communication in different energy-neutral Internet of Things (IoT) applications. Starting from the available energy budget, harvested through solar, radio frequency (RF) or capacitive harvesting, feasible hardware, modulation and Medium Access Control (MAC) solutions are explored.