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

GbyAI
GbyAI
Martin Fowler
Martin Fowler
I
InfoQ
腾讯CDC
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
爱范儿
爱范儿
Microsoft Security Blog
Microsoft Security Blog
Google DeepMind News
Google DeepMind News
D
DataBreaches.Net
云风的 BLOG
云风的 BLOG
F
Fortinet All Blogs
N
Netflix TechBlog - Medium
博客园 - 聂微东
Microsoft Azure Blog
Microsoft Azure Blog
D
Docker
博客园 - 三生石上(FineUI控件)
Y
Y Combinator Blog
博客园 - Franky
Engineering at Meta
Engineering at Meta
B
Blog
罗磊的独立博客
Apple Machine Learning Research
Apple Machine Learning Research
Jina AI
Jina AI
V
Visual Studio Blog

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
Analog Computing for Signal Processing and Communications...
Matteo Nerini, Bruno Clerckx · 2025-04-10 · via eess.SP updates on arXiv.org

Analog-domain operations offer a promising solution to accelerating signal processing and enabling future multiple-input multiple-output (MIMO) communications with thousands of antennas. In Part I of this paper, we have introduced a microwave linear analog computer (MiLAC) as an analog computer that processes microwave signals linearly, demonstrating its potential to reduce the computational complexity of specific signal processing tasks. In Part II of this paper, we extend these benefits to wireless communications, showcasing how MiLAC enables gigantic MIMO beamforming entirely in the analog domain. MiLAC-aided beamforming enables the maximum flexibility and performance of digital beamforming, while significantly reducing hardware costs by minimizing the number of radio-frequency (RF) chains and only relying on low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In addition, it eliminates per-symbol operations by completely avoiding digital-domain processing and remarkably reduces the computational complexity of zero-forcing (ZF), which scales quadratically with the number of antennas instead of cubically. It also processes signals with fixed matrices, e.g., the discrete Fourier transform (DFT), directly in the analog domain. Numerical results show that it can perform ZF and DFT with a computational complexity reduction of up to $1.5\times 10^4$ and $4.0\times 10^7$ times, respectively, compared to digital beamforming.