arXiv (All)
open access
Geometry-Informed Neural Network for Pilot-Limited Channel Estimation via Sensing from Uplink Traffic
Reliable wireless systems depend on accurate uplink channel estimation for precoding, scheduling, and link adaptation at the base station. In the upper mid-band, wide bandwidths and large antenna arrays increase the channel dimension and make pilot-efficient estimation particularly challenging. Whil
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
- Simultaneous Direct and Indirect Channel Estimation for RIS-Assisted MIMO Communications#1296599
- User Association and Transmit Beamforming for Rotatable Antenna-Enabled LAE Networks#1129614
- Sensing-Assisted OFDM with Index Modulation: A Waveform for Optical Wireless Integrated Communication and Localisation#1357408
- Energy-efficient RF source power control for opportunistic distributed sensing in wireless passive sensor networks#955142
- Time-Varying Bayesian Optimization Without a Metronome#1077831
Record · ID 1233031
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