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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
RIS-Aided mmWave O-RAN: Coverage Extension and User Mobil...
Tawfik Osman, Aditya S. Shekhawat, Abhradeep Roy, Georgios C. Tr · 2025-10-23 · via eess.SP updates on arXiv.org

Reconfigurable Intelligent Surfaces (RISs) can redirect electromagnetic waves to desired directions to enhance signal coverage and/or improve signal-to-noise ratio (SNR) at the user equipment (UE). We present the design, implementation, and evaluation of an RIS-assisted O-RAN 5G system operating in the FR2 millimeter wave (mmWave) frequency band. We first introduce the design of 1,024 element (32 $\times$ 32) 1-bit RIS operating at the 28 GHz band, utilizing a modular and scalable tiled architecture. Then we demonstrate how the O-RAN E2 interface can be leveraged to dynamically control RIS configurations without modifying standard 5G signaling procedures. To evaluate the RIS-assisted 5G system, we conducted extensive field trials in both indoor and outdoor environments. The results of the O-RAN link coverage trials show that the deployed RIS provides substantial received signal power gains, ranging from 9 to 20 dB and 6 to 18 dB in indoor and outdoors scenarios, respectively. Handling UE mobility in RIS-assisted systems is challenging due to the need for joint RIS and UE beam management. For that, we develop two UE mobility management algorithms and evaluate them in real-time operation using the RIS O-RAN testbed. These algorithms leverage the received signal power at the UE to jointly track and adapt the RIS and UE beams in real time as the UE moves. The findings draw important insights into the practical feasibility of integrating RIS into O-RAN systems to enhance coverage, mobility support, and link reliability in next-generation cellular networks.