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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
Multi-Segment Photonic Power Converters for Energy Harves...
Othman Younus, Behnaz Majlesein, Richard Nacke, Isaac N. O. Osah · 2025-10-08 · via eess.SP updates on arXiv.org

The demand for energy-efficient high-speed wireless communication, coupled with the rapid rise of IoT devices, requires systems that integrate power harvesting with optical data reception to eliminate the need for charging or battery replacements. Recent advances have explored the use of solar cells as optical receivers for high-speed data detection alongside power harvesting. \acs{GaAs}-based \acp{PPC} provide six times greater electron mobility than silicon- or cadmium telluride-based cells, enabling faster data detection and improved power efficiency. However, their bandwidth is constrained by junction capacitance, which increases with active area, creating a trade-off between power output and data rate. To address this, we propose and test multi-segment \acs{GaAs}-based \Acp{PPC} that serve as both energy harvesters and data detectors. By segmenting the active area into 2, 4, or 6 subcells, forming circular areas with diameters of 1, 1.5, or 2.08~mm, we reduce capacitance and boost bandwidth while preserving light collection. Fabricated on a semi-insulating \ac{GaAs} substrate with etched trenches for electrical isolation, the series-connected subcells optimize absorption and minimize parasitic effects. The \Acp{PPC} were used for an eye-safe 1.5~m optical wireless link, employing \ac{OFDM} with adaptive bit and power loading. The system achieved a world record data rate of 3.8~Gbps, which is four times higher than prior works. The system converts 39.7\% of optical power from a beam of 2.3~mW, although the segmentation increases the sensitivity of the alignment. These findings provide new solutions for off-grid backhaul for future communication networks, such as 6th generation (6G) cellular.