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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
Multi-user Beam-Alignment for Millimeter-Wave Networks
Rana A. Hassan, Nicolo Michelusi · 2018-02-17 · via eess.SP updates on arXiv.org

Millimeter-wave communications is the most promising technology for next-generation cellular wireless systems, thanks to the large bandwidth available compared to sub-6 GHz networks. Nevertheless, communication at these frequencies requires narrow beams via massive MIMO and beamforming to overcome the strong signal attenuation, and thus precise beam-alignment between transmitter and receiver is needed. The resulting signaling overhead may become a severe impairment, especially in mobile networks with high users density. Therefore, it is imperative to optimize the beam-alignment protocol to minimize the signaling overhead. In this paper, the design of energy efficient joint beam-alignment protocols for two users is addressed, with the goal to minimize the power consumption during data transmission, subject to rate constraints for both users, under analog beamforming constraints. It is proved that a bisection search algorithm is optimal. Additionally, the optimal scheduling strategy of the two users in the data communication phase is optimized based on the outcome of beam-alignment, according to a time division multiplexing scheme. The numerical results show significant decrease in the power consumption for the proposed joint beam-alignment scheme compared to exhaustive search and a single-user beam-alignment scheme taking place separately for each user.