惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

MyScale Blog
MyScale Blog
A
About on SuperTechFans
G
Google Developers Blog
B
Blog RSS Feed
F
Fortinet All Blogs
WordPress大学
WordPress大学
Recent Announcements
Recent Announcements
Hugging Face - Blog
Hugging Face - Blog
Y
Y Combinator Blog
MongoDB | Blog
MongoDB | Blog
小众软件
小众软件
人人都是产品经理
人人都是产品经理
博客园 - 叶小钗
T
The Blog of Author Tim Ferriss
Jina AI
Jina AI
IT之家
IT之家
P
Proofpoint News Feed
美团技术团队
量子位
Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
B
Blog
有赞技术团队
有赞技术团队
U
Unit 42

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
Rate-Splitting-Inspired Uplink ISAC: A Rate-Region Analysis
[Submitted on 5 Jun 2026 (v1), last revised 2 Sep 2026 (this ver · 2026-06-05 · via eess.SP updates on arXiv.org

View PDF HTML (experimental)

Abstract:Integrated sensing and communication (ISAC) enables sensing and communication (S&C) functionalities to share spectrum, hardware, and signal-processing resources, but the resulting inter-functionality interference creates a fundamental receiver-design challenge in uplink operation. To this end, rate-splitting (RS)-inspired ISAC has been proposed as a flexible approach to inter-functionality interference management, whereby communication interference during sensing is partially decoded and cancelled and partially treated as noise. In this work, we characterize the Pareto-optimal communication-rate (CR)-sensing-rate (SR) rate region of RS-inspired uplink ISAC by jointly optimizing the sensing illumination and communication-message split. Closed-form CR and SR expressions are derived while accounting for residual sensing-echo interference caused by target-response estimation uncertainty. We analytically prove that the resulting RS-inspired region contains the non-orthogonal multiple access (NOMA)-inspired endpoint-order time-sharing region. We further show that, under fixed sensing illumination, residual sensing-echo interference can break the containment of the orthogonal multiple access (OMA)-inspired region by both the RS- and NOMA-inspired regions. Joint sensing-illumination optimization enlarges these non-orthogonal achievable regions and recovers the maximum CR at the zero-SR endpoint. High-signal-to-noise ratio (SNR) and near-field large-array analyses characterize the asymptotic behaviour, and numerical results validate the analysis under both near- and far-field propagation. This is the first work to characterize the Pareto-optimal rate region of RS-inspired uplink ISAC.

Submission history

From: Anup Mishra [view email]
[v1] Fri, 5 Jun 2026 09:36:43 UTC (487 KB)
[v2] Wed, 2 Sep 2026 18:01:53 UTC (773 KB)