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
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园_首页
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
Docker
酷 壳 – CoolShell
酷 壳 – CoolShell
宝玉的分享
宝玉的分享
Martin Fowler
Martin Fowler
美团技术团队
量子位
M
MIT News - Artificial intelligence
Apple Machine Learning Research
Apple Machine Learning Research
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
博客园 - 三生石上(FineUI控件)
腾讯CDC
Hugging Face - Blog
Hugging Face - Blog
博客园 - 【当耐特】
小众软件
小众软件
博客园 - 司徒正美
罗磊的独立博客
云风的 BLOG
云风的 BLOG
B
Blog RSS Feed
博客园 - 聂微东

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
Wideband Quantum Transduction for Rydberg Atomic Receiver...
[Submitted on 15 Feb 2026 (v1), last revised 8 Jul 2026 (this ve · 2026-02-15 · via eess.SP updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper investigates a six-wave mixing (SWM)-based Rydberg atomic receiver as a wideband radio frequency (RF)-to-optical quantum transducer. Specifically, we develop an explicit baseband input-output model that bridges the RF-induced atomic coherence to the detected optical readout. Based on the exact detected SWM response, we develop a reduced-order closed-form two-pole low-pass approximation under the near-resonant weak-signal of interest, which provides an analytical insight into how the 3-dB bandwidth is manipulated by the dressed higher-level atomic dynamics and optical/RF parameters. The validity range of this approximation is then quantified to clarify the operating conditions under which this reduced-order model accurately represents the exact SWM response. We further characterize the linear dynamic range by employing the 1-dB compression point (P1dB) and the input-referred third-order intercept point (IIP3), unveiling a communication-compatible characterization of the bandwidth-sensitivity-linearity trade-off. Extensive simulation results demonstrate that SWM can achieve a 3-dB bandwidth of approximately 10 MHz while maintaining favorable linearity and sensitivity under the strict low-pass condition. The comparison with the EIT regime indicates that the two schemes should be treated as complementary rather than universally ordered. From an engineering perspective, the preferred SWM operating region is therefore not the one with the largest bandwidth, but the one that simultaneously provides a large bandwidth, acceptable sensitivity, favorable linearity, and low-pass regularity.

Submission history

From: Yuanbin Chen [view email]
[v1] Sun, 15 Feb 2026 02:08:30 UTC (2,542 KB)
[v2] Wed, 8 Jul 2026 09:33:08 UTC (1,635 KB)