Uncertainty-Weighted Experience Replay for Continual MIMO Channel Prediction

2026-04-16T08:51:45Z5ece52fa8d2e7f890d88c293c54d747690682d5dbfa5d8eeedb095eead744bec
BD‑RISBussgang decompositionCSI predictionLARS replayLLMLSTMLoRaMIMOMonte‑Carlo dropoutNCR swarmReconfigurable Intelligent SurfaceRician multipath fadingUW‑ERactive RISagent coordinationbandwidth reductioncontinual learningcovert channelsembodied agentshybrid transmit/reflectnetwork‑controlled repeatersnoncoherent ML detectionpower amplifier nonlinearitysemantic communicationuncertainty estimation

What happened

Collection of recent signal‑processing and wireless communications papers focused on improved channel/state prediction, resource‑efficient sensing and communication, and ML‑enabled receivers and trackers. Key topics include uncertainty‑weighted continual learning for MIMO CSI prediction (UW‑ER with MC‑dropout), semantic compression for embodied agent communications (LLM‑based semantic processor and importance‑aware transmission), active/beyond‑diagonal RIS with hybrid transmit/reflect modes, fluid‑antenna hybrid analog‑digital DOA estimation (FA‑HAD), context‑aware CSI inference via cVAE forAP

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_eess_sp
Record identifier
5ece52fa8d2e7f890d88c293c54d747690682d5dbfa5d8eeedb095eead744bec
Enrichment time
2026-04-16T08:51:45Z
AI-assisted enrichment
Yes

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