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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
Efficient Spectrum Utilization via Pulse Shape Design for...
Elena-Iulia Dobre, Ayman Mostafa, Lutz Lampe, Hoda ShahMohammadi · 2020-10-11 · via eess.SP updates on arXiv.org

Microwave backhaul links are characterized by high signal-to-noise ratios permitting spectrally-efficient transmission. The used signal constellation sizes and achievable data rates are typically limited by transceiver impairments, predominantly by phase noise from non-ideal carrier generation. In this paper, we propose a new method to improve the data rate over such microwave links. We make use of the fact that adjacent frequency channels are inactive in many deployment scenarios. We argue that additional data can be transmitted in the skirts of the spectral mask imposed on the transmission signal by regulation. To accomplish this task, we present a shaped wideband single-carrier transmission using non-Nyquist pulse shapes. In particular, we design spectrum-skirt filling (SSF) pulse shaping filters that follow the spectral mask response, and perform detection using an accordingly increased sampling frequency at the receiver. We evaluate the achievable information rates of the SSF-based transmission considering practical dispersive channels and non-ideal transmitter and receiver processing. To compensate for phase noise impairments, we derive carrier phase tracking and estimation techniques, and utilize them in tandem with nonlinear precoding which mitigates the intersymbol interference introduced by the non-Nyquist SSF shaping filter. Quantitative performance evaluations show that the proposed system design achieves higher data rates in a dispersive microwave propagation environment with respect to the conventional transmission with Nyquist pulse shaping.