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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
Power Domain Sparse Dimensional Constellation Multiple Ac...
Yuhao Lian, Xiao Han, Xinmao Deng · 2025-06-09 · via eess.SP updates on arXiv.org

With the commercial deployment of 5G and the in-depth research of 6G, the demand for high-speed data services in the next-generation fiber optic access systems is growing increasingly. Passive optical networks (PONs) have become a research hotspot due to their characteristics of low loss, high bandwidth, and low cost. However, the traditional orthogonal multiple access (OMA-PON) has difficulty meeting the requirements of the next-generation PON for high spectral efficiency and flexibility. In this paper, a novel transmission technology, namely power-domain sparse dimension constellation multiple access (PD-SDCMA), is proposed for the first time. Through the signal space dimension selection strategy (S2D-strategy) in the high-dimensional signal space, the low-dimensional constellation is sparsely superimposed into the high-dimensional space, thereby reducing multi-user interference and enhancing the system capacity. PD-SDCMA supports higher-order modulation formats and more access groups, and is also compatible with the existing orthogonal frequency division multiplexing (OFDM) architecture. The simulation results show that in a 25 km single-mode fiber system, compared with PD-NOMA and 3D-NOMA, PD-SDCMA can support more users and significantly reduce BER. This technology provides an efficient and low-cost solution for the evolution of Flexible PONs.