FM-RME: Foundation Model Empowered Radio Map Estimation
2026-03-04T19:57:33Z•390025a2a5cbe75ee0bd843b080ae86990634a5e40dfb6a7bf65dca19733f483
ADCCSIDACLLM-agentsO-RANRF-fingerprintingXAIadversarial-MLagentic-AIbeamformingchannel-estimationcode-releasedigital-twinfoundation-modelshardwaremmWaveprivacyquantizationradio-map-estimationradiometrysignal-processingsupply-chaintrackingwirelesszero-shot
What happened
This feed collects recent arXiv preprints (Feb 27, 2026) focused on wireless communications, RF sensing, signal processing, and ML/AI for radio systems. Highlights include: FM-RME, a foundation-model approach for multi-dimensional radio-map estimation with zero-shot inference; CSI-RFF, which extracts stable per-device micro-signals from commodity Wi‑Fi CSI enabling high-accuracy RF fingerprinting (open-set authentication); ClusterCKM and other works improving channel estimation via environment knowledge and X-REFINE, an XAI-driven input-filtering and architecture fine-tuning approach; PAO, a “
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 390025a2a5cbe75ee0bd843b080ae86990634a5e40dfb6a7bf65dca19733f483
- Enrichment time
- 2026-03-04T19:57:33Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.