arXiv (All)
open access
OTFS Channel Estimation Utilizing Sparse Bayesian Generative Modelling
One of the key challenges of future wireless communication systems is ensuring reliability in high-speed mobile scenarios, where accurate recovery of channel state information (CSI) is essential. Many recent studies have concluded that orthogonal time-frequency space (OTFS) modulation is a promising
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
- Data-Aided Bayesian Learning for CSI Estimation over Doubly-Selective DCO-OTFS MIMO VLC Channels with Affine-Precoded Superimposed Training Sequences#1036995
- Learning to Transmit Over Unknown Erasure Channels with Empirical Erasure Rate Feedback#793456
- Learning to Transmit Over Unknown Erasure Channels with Empirical Erasure Rate Feedback#789452
- GPU-Accelerated Robust Beamforming for OTFS Massive MIMO with CSI Uncertainty#1129385
- Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing#924919
Record · ID 821057
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