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
G
Google Developers Blog
Blog — PlanetScale
Blog — PlanetScale
P
Proofpoint News Feed
博客园 - Franky
L
LangChain Blog
GbyAI
GbyAI
A
About on SuperTechFans
MongoDB | Blog
MongoDB | Blog
F
Fortinet All Blogs
Y
Y Combinator Blog
Stack Overflow Blog
Stack Overflow Blog
博客园 - 叶小钗
N
Netflix TechBlog - Medium
D
DataBreaches.Net
Martin Fowler
Martin Fowler
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Hugging Face - Blog
Hugging Face - Blog
博客园_首页
爱范儿
爱范儿
罗磊的独立博客
H
Help Net Security
云风的 BLOG
云风的 BLOG
C
Check Point 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
Novel Double-Chirp Preamble Design for Multiuser Asynchro...
[Submitted on 19 Dec 2025 (v1), last revised 27 Aug 2026 (this v · 2025-12-20 · via eess.SP updates on arXiv.org

View PDF HTML (experimental)

Abstract:This paper proposes a novel preamble design and detection method for multiuser asynchronous massive MIMO LoRa networks. Unlike existing works, which only consider the preamble detection for a single end device (ED), we propose to simultaneously detect the preambles of multiple EDs that asynchronously transmit their uplink (UL) packets to a multiple-antenna gateway (GW). First, we show that the preamble detection in multiuser LoRa networks with the conventional single-chirp preamble suffers from the so-called preamble resemblance effect. This means that the preamble of any single ED can resemble the preambles of all EDs in the network and make it impossible to determine to which ED a preamble belongs. To address this problem, a novel double-chirp preamble design and a preamble assignment method are proposed, which can mitigate the preamble resemblance effect by making the preamble of each ED unique and recognizable. Next, a maximum-likelihood (ML) based detection scheme for the proposed double-chirp preamble is derived. Finally, since the proposed algorithm requires the calculation of the discrete Fourier transform (DFT) every sampling period, we propose a low-complexity technique to calculate the DFT recursively to reduce the complexity of our proposed design. Simulation shows that the proposed preamble design and detection require just about 2 dB more power to increase the number of EDs from one to 15 in the Rayleigh fading channel while achieving the same preamble detection error performance.

Submission history

From: Khai Nguyen [view email]
[v1] Fri, 19 Dec 2025 17:43:45 UTC (322 KB)
[v2] Thu, 27 Aug 2026 18:03:15 UTC (317 KB)