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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
Mixed RF/FSO Cooperative Relaying Systems with Co-Channel...
Elyes Balti, Mohsen Guizani · 2019-01-16 · via eess.SP updates on arXiv.org

In this paper, we provide a global framework analysis of a dual-hop mixed Radio Frequency (RF)/Free Space Optical (FSO) system with multiple branches/relays wherein the first and second hops, respectively, consist of RF and FSO channels. To cover various cases of fading, we propose generalized channels' models for RF and FSO links that follow the Nakagami-m and the Double Generalized Gamma (DGG) distributions, respectively. Moreover, we suggest Channel State Information (CSI)-assisted relaying or variable relaying gain based Amplifiy-and-Forward (AF) amplification. Partial relay selection with outdated CSI is assumed as a relay selection protocol based on the knowledge of the RF CSI. In order to derive the end-to-end Signal-to-Interference-plus-Noise Ratio (SINR) statistics such as the Cumulative Distribution Function (CDF), the Probability Density Function (PDF), the higher order moments, the amount of fading and the Moment Generating Function (MGF), the numerical values of the fading severity parameters are only valid for integer values. Based on these statistics, we derive closed-forms of the outage probability, the bit error probability, the ergodic capacity and the outage capacity in terms of Meijer-G, univariate, bivariate and trivariate Fox-H functions. Capitalizing on these expressions, we derive the asymptotic high SNR to unpack valuable engineering insights of the system performance. Monte Carlo simulation is used to confirm the analytical expressions.