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

A
About on SuperTechFans
G
Google Developers Blog
L
LangChain Blog
aimingoo的专栏
aimingoo的专栏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
云风的 BLOG
云风的 BLOG
小众软件
小众软件
月光博客
月光博客
Recent Announcements
Recent Announcements
人人都是产品经理
人人都是产品经理
P
Proofpoint News Feed
博客园 - 聂微东
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
雷峰网
雷峰网
The Cloudflare Blog
博客园_首页
美团技术团队
大猫的无限游戏
大猫的无限游戏
B
Blog
IT之家
IT之家
Jina AI
Jina AI
H
Hackread – Cybersecurity News, Data Breaches, AI and More
C
Check Point 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

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
Map-Assisted Constellation Design for mmWave WDM with OAM...
Yuan Wang, Chen Gong, Nuo Huang, Zhengyuan Xu · 2021-11-04 · via eess.SP updates on arXiv.org

We consider a system that integrates positioning and single-user millimeter wave (mmWave) communication, where the communication part adopts wavelength division multiplexing (WDM) and orbital angular momentum (OAM). This paper addresses the multi-dimensional constellation design in shortrange line-of-sight (LOS) environment, with stable communication links. We propose a map-assisted method to quantify the system parameters based on positions and reduce real-time computing overhead. We explore the possibility of using a few patterns in the maps, and investigate its performance loss. We first investigate the features of OAM beams, and find that the link gain ratio between any two sub-channels remains unchanged at some postions. Then, we prove that a fixed constellation can be adopted for the positions where the link gain matrices are sufficiently close to be proportional. Moreover, we prove that the system can adopt a fixed power vector to generate a multidimensional constellation if the difference between fixed power vector and optimal power vector is small. Finally, we figure out that the constellation design for all receiver locations can be represented by a few constellation sets.