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

Recent Announcements
Recent Announcements
人人都是产品经理
人人都是产品经理
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
GbyAI
GbyAI
博客园 - 司徒正美
美团技术团队
Vercel News
Vercel News
IT之家
IT之家
U
Unit 42
Y
Y Combinator Blog
罗磊的独立博客
Microsoft Security Blog
Microsoft Security Blog
MongoDB | Blog
MongoDB | Blog
Jina AI
Jina AI
V
Visual Studio Blog
B
Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
MyScale Blog
MyScale Blog
博客园 - 叶小钗
A
About on SuperTechFans
WordPress大学
WordPress大学
Hugging Face - Blog
Hugging Face - Blog
B
Blog RSS Feed

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
Coexistence of Radar and Communication with Rate-Splittin...
Anup Mishra, Israel Leyva-Mayorga, Petar Popovski · 2024-11-20 · via eess.SP updates on arXiv.org

Future wireless networks are envisioned to facilitate the seamless coexistence of communication and sensing functionalities, thereby enabling the much-touted integrated sensing and communication (ISAC) paradigm. A key challenge in ISAC is managing inter-functionality interference while maintaining a balanced performance trade-off. In this work, we propose a rate-splitting (RS)-inspired approach to address this challenge in an uplink ISAC scenario, where a base station (BS) serves an uplink communication user while detecting a radar target. We derive inner bounds on the ergodic data information rate for the communication user and the ergodic radar estimation information rate for the sensing target. A closed-form solution is also derived for the optimal power split in RS that maximizes the communication user's performance. Compared to orthogonal multiple access (OMA)- and non-orthogonal multiple access (NOMA)-inspired approaches, the proposed approach achieves a more favorable sensing-communication trade-off by virtue of the decoding order flexibility introduced through splitting the communication message. Notably, this is the first work to employ an RS-inspired strategy as a general framework for non-orthogonal coexistence of sensing and communication, extending its applicability beyond traditional digital-only settings.