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

爱范儿
爱范儿
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
WordPress大学
WordPress大学
Y
Y Combinator Blog
I
InfoQ
美团技术团队
罗磊的独立博客
B
Blog RSS Feed
GbyAI
GbyAI
小众软件
小众软件
IT之家
IT之家
Engineering at Meta
Engineering at Meta
Blog — PlanetScale
Blog — PlanetScale
V
V2EX
Last Week in AI
Last Week in AI
酷 壳 – CoolShell
酷 壳 – CoolShell
Jina AI
Jina AI
MyScale Blog
MyScale Blog
博客园 - 聂微东
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
Apple Machine Learning Research
Apple Machine Learning Research
The GitHub Blog
The GitHub Blog
T
The Blog of Author Tim Ferriss

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
Energy Efficiency Optimization in Radar-Communication Spe...
Emanuele Grossi, Marco Lops, Luca Venturino · 2021-04-19 · via eess.SP updates on arXiv.org

Energy efficiency, possibly coupled with cognition-based and spectrum-sharing architectures, is a key enabling technology for green communications in 5G-and-beyond standards. In this context, the present paper considers a multiple-input multiple-output communication system cooperatively coexisting with a surveillance radar: the objective function is the communication system energy efficiency, while radar operation is safeguarded by constraining the minimum received signal-to-disturbance ratio for a set of range-azimuth cells of the controlled scene, and no time synchronization between them is assumed. The degrees of freedom are the transmit powers of both systems, the space-time communication codebook and the linear filters at the radar receiver. The resulting optimization problem is non-convex, due to both the objective function and the presence of signal-dependent interference (clutter): we develop a block-coordinate-ascent approximate solution, and offer a thorough performance assessment, so as to elicit the merits of the proposed approach, along with the interplay among the achievable energy efficiency, the density of scatterers in the environment, and the size of the set of protected radar cells.