arXiv (All)
open access
Reliable Narrowband Interference Detection via Backward Conformal Prediction
Narrowband interference can severely degrade the performance of wireless links by concentrating significant power on a small portion of the channel. Machine learning detectors trained on baseband I/Q samples can identify the affected subcarriers with high accuracy, surpassing model-based detectors t
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
- Advanced Path Loss Estimation at 5.8 GHz in Heterogeneous Urban Environments through Hybrid Modeling and Machine Learning#1360542
- Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case#133951
- Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices#612811
- Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices#611334
- Low-Power Wide-Area Network Over White Spaces#163684
Record · ID 1249378
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