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

博客园 - 司徒正美
月光博客
月光博客
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
美团技术团队
WordPress大学
WordPress大学
罗磊的独立博客
Last Week in AI
Last Week in AI
人人都是产品经理
人人都是产品经理
爱范儿
爱范儿
MongoDB | Blog
MongoDB | Blog
J
Java Code Geeks
H
Hackread – Cybersecurity News, Data Breaches, AI and More
H
Help Net Security
S
SegmentFault 最新的问题
C
Check Point Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
腾讯CDC
Engineering at Meta
Engineering at Meta
The GitHub Blog
The GitHub Blog
F
Fortinet All Blogs
D
DataBreaches.Net
雷峰网
雷峰网
GbyAI
GbyAI
宝玉的分享
宝玉的分享

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
Centralized active reconfigurable intelligent surface: Ar...
Changhao Liu, Fan Yang, Shenheng Xu, Yezhen Li, Maokun Li · 2024-01-18 · via eess.SP updates on arXiv.org

Reconfigurable intelligent surfaces (RISs) are promising candidate for the 6G communication. Recently, active RIS has been proposed to compensate the multiplicative fading effect inherent in passive RISs. However, conventional distributed active RISs, with at least one amplifier per element, are costly, complex, and power-intensive. To address these challenges, this paper proposes a novel architecture of active RIS: the centralized active RIS (CA-RIS), which amplifies the energy using a centralized amplifying reflector to reduce the number of amplifiers. Under this architecture, only as low as one amplifier is needed for power amplification of the entire array, which can eliminate the mutual-coupling effect among amplifiers, and significantly reduce the cost, noise level, and power consumption. We evaluate the performance of CA-RIS, specifically its path loss, and compare it with conventional passive RISs, revealing a moderate amplification gain. Furthermore, the proposed CA-RIS and the path loss model are experimentally verified, achieving a 9.6 dB net gain over passive RIS at 4 GHz. The CA-RIS offers a substantial simplification of active RIS architecture while preserving performance, striking an optimal balance between system complexity and the performance, which is competitive in various scenarios.