arXiv (All)
open access
Adversarial Pilot Injection Against AI-Based CSI Feedback in FDD Massive MIMO Systems
Artificial intelligence (AI)-based channel state information (CSI) feedback reduces overhead in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, yet its reconstructed CSI depends on the integrity of pilot acquisition. This paper identifies and evaluates the a
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
- Mean Spatial Frequency Decoupling for Learning-Based Uplink-to-Downlink Covariance Conversion in FDD Massive MIMO#1155531
- Prior-Aided Masked Vector Quantization CSI Feedback for FDD Massive MIMO Systems#1011491
- Compensating Coarse Quantization in Massive MIMO: Channel Estimation and BER under Imperfect CSI#925088
- Compensating Coarse Quantization in Massive MIMO: Channel Estimation and BER under Imperfect CSI#920854
- Joint Identification, Transmission, and Sensing over State-Dependent Channels with Noiseless Feedback#1354469
Record · ID 1491837
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