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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 Learning Coverage- and Power-Optimal Transmitter Placement from Building Maps: A Comparative Study of Direct and Indirect Neural Approaches Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems Low-Rank Adaptation Redux for Large Models Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels How Well Can We Decode Vowels from Auditory EEG -- A Rigorous Cross-Subject Benchmark with Honest Assessment FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning SAGE: Training-Free Semantic Evidence Composition for Edge-Cloud Inference under Hard Uplink Budgets A Hybrid Windkessel-Neural Approach for Improved Noninvasive Blood Pressure Monitoring Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks Deep Learning for Multi-Antenna Modulation Recognition of Radio Signals AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G What Physics do Data-Driven MoCap-to-Radar Models Learn? TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction ECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset AI-Enabled Covert Channel Detection in RF Receiver Architectures Temporal Cross-Modal Knowledge-Distillation-Based Transfer-Learning for Gas Turbine Vibration Fault Detection Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain The Existential Theory of Research: Why Discovery Is Hard Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding BioTrain: Sub-MB, Sub-50mW On-Device Fine-Tuning for Edge-AI on Biosignals Applied AI-Enhanced RF Interference Rejection From Equations to Algorithms and Data: Transforming Microwave Engineering and Education with Machine Learning RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohorts A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks WearBCI Dataset: Understanding and Benchmarking Real-World Wearable Brain-Computer Interfaces Signals An Edge-Cloud Collaborative Architecture for Proactive Elderly Care: Real-Time Risk Assessment and Three-Level Emergency Response A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum on Commodity Laptops LiveSense: A Real-Time Wi-Fi Sensing Platform for Range-Doppler on COTS Laptop WST-X Series: Wavelet Scattering Transform for Interpretable Speech Deepfake Detection Real-Time Streamable Generative Speech Restoration with Flow Matching Concurrence: A dependence criterion for time series, applied to biological data PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData Feedback Lunch: Learned Feedback Codes for Secure Communications StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs Distributed Associative Memory via Online Convex Optimization Networks of Causal Abstractions: A Sheaf-theoretic Framework Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention Biased Federated Learning under Wireless Heterogeneity Drivetrain simulation using variational autoencoders Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformation
Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G
Michele Polese, Niloofar Mohamadi, Salvatore D'Oro, Leonardo Bon · 2025-07-09 · via eess.SP updates on arXiv.org

Data-intensive Artificial Intelligence (AI) applications at the network edge demand a fundamental shift in Radio Access Network (RAN) design, from merely consuming AI for network optimization, to actively enabling distributed AI workloads. This presents a significant opportunity for network operators to monetize AI while leveraging existing infrastructure. To realize this vision, this article presents a novel converged O-RAN and AI-RAN architecture for unified orchestration and management of telecommunications and AI workloads on shared infrastructure. The proposed architecture extends the Open RAN principles of modularity, disaggregation, and cloud-nativeness to support heterogeneous AI deployments. We introduce two key architectural innovations: (i) the AI-RAN Orchestrator, which extends the O-RAN Service Management and Orchestration (SMO) to enable integrated resource and allocation across RAN and AI workloads; and (ii) AI-RAN sites that provide distributed edge AI platforms with real-time processing capabilities. The proposed architecture enables flexible orchestration, meeting requirements for managing heterogeneous workloads at different time scales while maintaining open, standardized interfaces and multi-vendor interoperability.

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