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

酷 壳 – CoolShell
酷 壳 – CoolShell
G
Google Developers Blog
V
V2EX
美团技术团队
H
Help Net Security
月光博客
月光博客
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
The Cloudflare Blog
U
Unit 42
大猫的无限游戏
大猫的无限游戏
Recent Announcements
Recent Announcements
A
About on SuperTechFans
博客园 - Franky
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium
人人都是产品经理
人人都是产品经理
博客园 - 司徒正美
MyScale Blog
MyScale Blog
B
Blog
雷峰网
雷峰网
Y
Y Combinator Blog
云风的 BLOG
云风的 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
Decentralized Cooperative Beamforming for Networked LEO S...
Yuchen Zhang, Eva Lagunas, Xue Xian Zheng, Symeon Chatzinotas, T · 2025-12-22 · via eess.SP updates on arXiv.org

Inter-satellite-link-enabled low-Earth-orbit (LEO) satellite constellations are evolving toward networked architectures that support constellation-level cooperation, enabling multiple satellites to jointly serve user terminals through cooperative beamforming. While such cooperation can substantially enhance link budgets and achievable rates, its practical realization is challenged by the scalability limitations of centralized beamforming designs and the stringent computational and signaling constraints of large LEO constellations. This paper develops a fully decentralized cooperative beamforming framework for networked LEO satellite downlinks. Using an ergodic-rate-based formulation, we first derive a centralized weighted minimum mean squared error (WMMSE) solution as a performance benchmark. Building on this formulation, we propose a topology-agnostic decentralized beamforming algorithm by localizing the benchmark and exchanging a set of globally coupled variables whose dimensions are independent of the antenna number and enforcing consensus over arbitrary connected inter-satellite networks. The resulting algorithm admits fully parallel execution across satellites. To further enhance scalability, we eliminate the consensus-related auxiliary variables in closed form and derive a low-complexity per-satellite update rule that is optimal to local iteration and admits a quasi-closed-form solution via scalar line search. Simulation results show that the proposed decentralized schemes closely approach centralized performance under practical inter-satellite topologies, while significantly reducing computational complexity and signaling overhead, enabling scalable cooperative beamforming for large LEO constellations.