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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
Fundamental Limits of THz Inter-Satellite ISAC Under Hard...
Haofan Dong, Ozgur B. Akan · 2025-09-19 · via eess.SP updates on arXiv.org

This paper establishes a theoretical framework for analyzing the fundamental performance limits of terahertz (THz) Low Earth Orbit (LEO) inter-satellite link (ISL) Integrated Sensing and Communications (ISAC) systems. We develop a unified, end-to-end signal model that, jointly captures the effects of extreme orbital dynamics, cascaded non-ideal hardware impairments, and micro-radian beam pointing errors. Through Bayesian Cramér-Rao Lower Bound (BCRLB) analysis, we derive the ultimate sensing accuracy for range and range-rate, revealing a quadratic ($1/f_c^2$) improvement in estimation variance with carrier frequency, which is ultimately floored by signal-dependent hardware distortion. For communication, we show that system performance is not power-limited but hardware-limited, deriving a closed-form capacity ceiling under the joint effect of phase noise and PA nonlinearity: $C_{\text{sat}} = \log_2(1 + e^{-σ_φ^2}/Γ_{\text{eff}})$, where $Γ_{\text{eff}}$ is a proposed hardware quality factor. Our numerical results, based on state-of-the-art component data and the identified trade-offs, suggest that favorable operational conditions may exist in the sub-THz frequency range (200-600 GHz) where the quadratic sensing gain with frequency is balanced against hardware quality degradation. Power Amplifier (PA) nonlinearity emerges as the dominant performance bottleneck, exceeding other impairments by one to two orders of magnitude.