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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
CmWave and Sub-THz: Key Radio Enablers and Complementary ...
Mayur V. Katwe, Aryan Kaushik, Keshav Singh, Marco Di Renzo, Shu · 2024-06-26 · via eess.SP updates on arXiv.org

Sixth-generation (6G) networks are poised to revolutionize communication by exploring alternative spectrum options, aiming to capitalize on strengths while mitigating limitations in current fifth-generation (5G) spectrum. This paper explores the potential opportunities and emerging trends for cmWave and sub-THz spectra as key radio enablers. This paper poses and answers three key questions regarding motivation of additional spectrum to explore the strategic implementation and benefits of cmWave and sub-THz spectra. Also, we show using case studies how these complementary spectrum bands will enable new applications in 6G, such as integrated sensing and communication (ISAC), re-configurable intelligent surfaces (RIS) and non-terrestrial networks (NTN). Numerical simulations reveal that the ISAC performance of cmWave and sub-THz spectra outperforms that of existing 5G spectrum, including sub-6 GHz and mmWave. Additionally, we illustrate the effective interplay between RIS and NTN to counteract the effects of high attenuation at sub-THz frequencies. Finally, ongoing standardization endeavors, challenges and promising directions are elucidated for these complementary spectrum bands.