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

Stack Overflow Blog
Stack Overflow Blog
L
LangChain Blog
人人都是产品经理
人人都是产品经理
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Tailwind CSS Blog
N
Netflix TechBlog - Medium
Microsoft Security Blog
Microsoft Security Blog
J
Java Code Geeks
博客园 - 【当耐特】
量子位
有赞技术团队
有赞技术团队
Jina AI
Jina AI
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
博客园_首页
C
Check Point Blog
B
Blog RSS Feed
M
MIT News - Artificial intelligence
H
Help Net Security
雷峰网
雷峰网
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 聂微东
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
A
About on SuperTechFans
腾讯CDC

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
OTFS-based Robust MMSE Precoding Design in Over-the-air C...
Dongkai Zhou, Jing Guo, Siqiang Wang, Zhong Zheng, Zesong Fei, W · 2023-07-04 · via eess.SP updates on arXiv.org

Over-the-air computation (AirComp), as a data aggregation method that can improve network efficiency by exploiting the superposition characteristics of wireless channels, has received much attention recently. Meanwhile, the orthogonal time frequency space (OTFS) modulation can provide a strong Doppler resilience and facilitate reliable transmission for high-mobility communications. Hence, in this work, we investigate an OTFS-based AirComp system in the presence of time-frequency dual-selective channels. In particular, we commence from the development of a novel transmission framework for the considered system, where the pilot signal is sent together with data, and the channel estimation is implemented according to the echo from the access point to the sensor, thereby reducing the overhead of channel state information (CSI) feedback. Hereafter, based on the CSI estimated from the previous frame, a robust precoding matrix aiming at minimizing mean square error in the current frame is designed, which takes into account the estimation error from the receiver noise and the outdated CSI. The simulation results demonstrate the effectiveness of the proposed robust precoding scheme by comparing it with the non-robust precoding. The performance gain is more obvious in a high signal-to-noise ratio in case of large channel estimation errors.