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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
Enhancing Security of TAS/MRC Based Mixed RF-UOWC System ...
Md. Ibrahim, A. S. M. Badrudduza, Md. Shakhawat Hossen, Milton K · 2021-05-19 · via eess.SP updates on arXiv.org

Post commercial deployment of fifth-generation (5G) technologies, the consideration of sixth-generation (6G) networks is drawing remarkable attention from research communities. Researchers suggest that similar to 5G, 6G technology must be human-centric where high secrecy together with high data rate will be the key features. These challenges can be easily overcome utilizing PHY security techniques over high-frequency free-space or underwater optical wireless communication (UOWC) technologies. But in long-distance communication, turbulence components drastically affect the optical signals, leading to the invention of the combination of radio-frequency (RF) links with optical links. This work deals with the secrecy performance analysis of a mixed RF-UOWC system where an eavesdropper tries to intercept RF communications. RF and optical links undergo $η-μ$ and mixture exponential generalized Gamma distributions, respectively. To keep pace with the high data rate of optical technologies, we exploit the antenna selection scheme at the source and maximal ratio combining diversity at the relay and eavesdropper, while the eavesdropper is unaware of the antenna selection scheme. We derive closed-form expressions of average secrecy capacity, secrecy outage probability, and strictly positive secrecy capacity to demonstrate the impacts of the system parameters on the secrecy behavior. Finally, the expressions are corroborated via Monte-Carlo simulations.