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

大猫的无限游戏
大猫的无限游戏
D
DataBreaches.Net
M
MIT News - Artificial intelligence
量子位
N
Netflix TechBlog - Medium
The Cloudflare Blog
The GitHub Blog
The GitHub Blog
P
Proofpoint News Feed
人人都是产品经理
人人都是产品经理
B
Blog RSS Feed
B
Blog
博客园_首页
博客园 - Franky
MyScale Blog
MyScale Blog
有赞技术团队
有赞技术团队
Apple Machine Learning Research
Apple Machine Learning Research
MongoDB | Blog
MongoDB | Blog
云风的 BLOG
云风的 BLOG
爱范儿
爱范儿
H
Help Net Security
Y
Y Combinator Blog
Stack Overflow Blog
Stack Overflow 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
酷 壳 – CoolShell
酷 壳 – CoolShell

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
Decentralization Issues in Cell-free Massive MIMO Network...
Felip Riera-Palou, Guillem Femenias · 2019-09-27 · via eess.SP updates on arXiv.org

Cell-free massive MIMO (CF-M-MIMO) systems represent an evolution of the classical cellular architecture that has dominated the mobile landscape for decades. In CF-M-MIMO, a central processing unit (CPU) controls a multitude of access points (APs) that are irregularly scattered throughout the coverage area effectively becoming a fully distributed implementation of the M-MIMO technology. As such, it inherits many of the key properties that have made M-MIMO one of the physical layer pillars of 5G systems while opening the door to new features not available in M-MIMO. Among the latest is the possibility of performing the precoding at the CPU (centralized) or at the APs (distributed) with the former known to offer much better performance at the cost of having to collect all the relevant channel state information (CSI) at the CPU. Realistic deployments of cell-free systems are likely to require more than one CPU when the area to be covered is large, thus a critical issue that needs to be solved is how these multiple CPUs should be interconnected. This paper analyzes and proposes designs for different degrees of interconnectivity among the CPUs for the specific case of centralized zero-forcing precoding. Results show that a modest form of CPU interconnection can boost very significantly the max-min rate performance so prevalent in CF-M-MIMO architectures.