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

雷峰网
雷峰网
宝玉的分享
宝玉的分享
量子位
博客园 - Franky
The Cloudflare Blog
博客园 - 【当耐特】
Jina AI
Jina AI
Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
Microsoft Security Blog
Microsoft Security Blog
博客园 - 叶小钗
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
罗磊的独立博客
V
V2EX
MongoDB | Blog
MongoDB | Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
H
Help Net Security
博客园 - 聂微东
F
Fortinet All Blogs
GbyAI
GbyAI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
人人都是产品经理
人人都是产品经理

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
Acceleration and Parallelization Methods for ISRS EGN Model
Ruiyang Xia, Guanjun Gao, Zanshan Zhao, Haoyu Wang, Kun Wen, Dao · 2024-12-16 · via eess.SP updates on arXiv.org

The enhanced Gaussian noise (EGN) model, which accounts for inter-channel stimulated Raman scattering (ISRS), has been extensively utilized for evaluating nonlinear interference (NLI) within the C+L band. Compared to closed-form expressions and machine learning-based NLI evaluation models, it demonstrates broader applicability and its accuracy is not dependent on the support of large-scale datasets. However, its high computational complexity often results in lengthy computation times. Through analysis, the high-frequency oscillations of the four-wave mixing (FWM) efficiency factor integrand were identified as a primary factor limiting the computational speed of the ISRS EGN model. To address this issue, we propose an approximation method to derive a closed-form expression for the FWM efficiency factor, which provides both high accuracy and high computational efficiency. Numerical results demonstrate that the method proposed in this work could achieve low error levels under high ISRS influence levels, with an absolute mean error (MAE) of approximately 0.0033 dB. Furthermore, a parallel computation strategy targeting independent regions within the integration domain is proposed and optimized, which further significantly improves computing efficiency.