Radio Environment Mapping with World Models for Active Measurement Control: Should Networks Dream of Optimal Control?
2026-05-26T08:51:40Z•21770b55855792a622169f07a91bb03f8cf42aaf1ffd2d91bfc515f3a0ca96ed
Bluetooth Low EnergyFM errorGaussian process baselineLWM-CDEREMRSSI mappingV2XVCO tuningVLCactive measurementchirp predistortioncollective perceptioncontrastive learningcrystal-free BLEdataset similaritydigital twinfew-shot learningradar signal processingradio environment mappingsoftware-defined radio (SDR) integration","physical-layer emulattransferabilityvehicular communicationsvisible light communicationwireless foundation modelworld models
What happened
This feed collects recent eess-sp (electrical engineering & systems science — signal processing / communications) arXiv preprints focused on ML-driven and hardware-aware advances for wireless sensing, communications, and signal processing. Key themes: (1) active radio environment mapping using world-model-style dreaming for sample-efficient RSSI map reconstruction; (2) dataset-embedding and contrastive methods (LWM-CDE) to measure dataset similarity and improve model transferability for wireless foundation models; (3) using Visible Light Communication (VLC) to augment V2X/collective perception
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 21770b55855792a622169f07a91bb03f8cf42aaf1ffd2d91bfc515f3a0ca96ed
- Enrichment time
- 2026-05-26T08:51:40Z
- AI-assisted enrichment
- Yes
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