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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
On the Feasibility of Out-of-Band Spatial Channel Informa...
Peize Zhang, Pekka Kyösti, Katsuyuki Haneda, Pasi Koivumäki, Wei · 2022-08-04 · via eess.SP updates on arXiv.org

The rollout of millimeter-wave (mmWave) cellular network enables us to realize the full potential of 5G/6G with vastly improved throughput and ultra-low latency. MmWave communication relies on highly directional transmission, which significantly increase the training overhead for fine beam alignment. The concept of using out-of-band spatial information to aid mmWave beam search is developed when multi-band systems operating in parallel. The feasibility of leveraging low-band channel information for coarse estimation of high-band beam directions strongly depends on the spatial congruence between two frequency bands. In this paper, we try to provide insights into the answers of two important questions. First, how similar is the power angular spectra (PAS) of radio channels between two well-separated frequency bands? Then, what is the impact of practical system configurations on spatial channel similarity? Specifically, the beam direction-based metric is proposed to measure the power loss and number of false directions if out-of-band spatial information is used instead of in-band information. This metric is more practical and useful than comparing normalized PAS directly. Point cloud ray-tracing and measurement results across multiple frequency bands and environments show that the degree of spatial similarity of beamformed channels is related to antenna beamwidth, frequency gap, and radio link conditions.