arXiv (All)
open access
CSI Simulation: Why Additive Noise Fails and How to Fix It
Channel State Information (CSI) has become a widely used wireless channel sensing modality for applications such as indoor localization, activity recognition, and respiration monitoring. Because collecting labeled data under every target condition is impractical, training CSI-based models often reli
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Low-Rank Prior-Guided Rank-One Sensing for Efficient CSI Feedback#1064700
- Data-Aided Bayesian Learning for CSI Estimation over Doubly-Selective DCO-OTFS MIMO VLC Channels with Affine-Precoded Superimposed Training Sequences#1036995
- SPATIAL REGRESSION MODELS USING INTER-REGION DISTANCES IN A NON-RANDOM CONTEXT#941678
- Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction#1012555
- Subspace Based Identification of Errors-in-Variables Linear Descriptor Systems#799475
Record · ID 1049899
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive