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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
Near-Orthogonal Overlay Communications in LoS Channel Ena...
Yufei Zhao, Xiaoyan Ma, Yong Liang Guan, Yile Liu, Afkar Mohamed · 2024-07-27 · via eess.SP updates on arXiv.org

This paper introduces a groundbreaking Line-of-Sight (LoS) Multiple-Input Multiple-Output (MIMO) communication architecture leveraging non-traditional Orbital Angular Momentum (OAM) beams. Challenging the conventional paradigm of hollow-emitting OAM beams, this study presents an innovative OAM transmitter design that produces directional OAM beams without central energy voids, aligning their radiation patterns with those of conventional planar wave horn antennas. Within the main lobe of these antennas, the phase variation characteristics inherent to OAM beams are ingeniously maintained, linking different OAM modes to the linear wavefront variation gradients, thereby reducing channel correlation in LoS scenarios and significantly augmenting the channel capacity of LoS-MIMO frameworks. Empirical validations conducted through a meticulously designed LoS-MIMO experimental platform reveal significant improvements in channel correlation coefficients, communication stability, and Bit Error Rate (BER) compared to systems utilizing traditional planar wave antennas. The experiment results underscore the potential of the novel OAM-based system to improve current LoS-MIMO communication protocols, and offer both academic and engineering guidance for the construction of practical communication infrastructures. Beyond its immediate contributions, this paper underscores a pivotal shift in the field of communications, pointing out that traditional communication algorithms have primarily focused on baseband signal processing while often overlooking the electromagnetic characteristics of the physical world.