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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
Optical STAR-RIS-Aided VLC Systems: RSMA Versus NOMA
Omar Maraqa, Sylvester Aboagye, Telex M. N. Ngatched · 2023-10-30 · via eess.SP updates on arXiv.org

A critical concern within the realm of visible light communications (VLC) pertains to enhancing system data rate, particularly in scenarios where the direct line-of-sight (LoS) connection is obstructed by obstacles. The deployment of meta-surface-based simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) has emerged to combat challenging LoS blockage scenarios and to provide 360 coverage in radio-frequency wireless systems. Recently, the concept of optical simultaneous transmission and reflection reconfigurable intelligent surface (OSTAR-RIS) has been promoted for VLC systems. This work is dedicated to studying the performance of OSTAR-RIS in detail and unveiling the VLC system performance gain under such technology. Specifically, we propose a novel multi-user indoor VLC system that is assisted by OSTAR-RIS. To improve the sum rate performance of the proposed system, both power-domain non-orthogonal multiple access (NOMA) and rate splitting multiple access (RSMA) are investigated in this work. To realize this, a sum rate maximization problem that jointly optimizes the roll and yaw angles of the reflector elements as well as the refractive index of the refractor elements in OSTAR-RIS is formulated, solved, and evaluated. The maximization problem takes into account practical considerations, such as the presence of non-users (i.e., blockers) and the orientation of the recipient's device. The sine-cosine meta-heuristic algorithm is employed to get the optimal solution of the formulated non-convex optimization problem. Moreover, the study delves into the sum energy efficiency optimization of the proposed system. Simulation results indicate that the proposed OSTAR-RIS RSMA-aided VLC system outperforms the OSTAR-RIS NOMA-based VLC system in terms of both the sum rate and the sum energy efficiency.