A dataset for clustered federated learning to support context-dependent Channel State Information decoding. The dataset was authored by Heasung Kim and harvested from the Texas Data Repository via Dataverse. It was last updated on October 15, 2025.
Use Cases
- Training context-dependent CSI feedback autoencoders based on the dataset's wireless channel measurements.
- Developing clustered federated learning algorithms for wireless networks based on the described CSI data.
- Benchmarking federated learning approaches for decentralized CSI decoding tasks.
Strengths
- Dataset is specifically designed for the niche research area of clustered federated learning for CSI decoding.
- Author and repository provenance (Heasung Kim, Texas Data Repository) are clearly stated.
- Update timestamp (2025-10-15) is recent, indicating active maintenance.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and file size are unknown, which may limit suitability assessment.
Provenance
- Source
- Texas Data Repository Harvested Dataverse
- Collection Method
- Likely contains simulated or measured wireless Channel State Information data.
- Time Range
- null
- Freshness
- Last updated 2025-10-15 03:42:22; freshness should be verified.
- Geography
- null