Large Multimodal Model-Based Environment-Aware Mobility Management
2026-07-14T08:51:47Z•928c5ae904e047a4d77506fb3fe22771d190652c524ec56c78408a13f9e4f25d
6GA_n^*CCMCKMCoxeter codesE6^*E7^*LMMRSMASCMASCMA-SVCautonomous drivingchannel capacity mapchannel knowledge mapclosest-point algorithmscoding theoryfanstarfinite-blocklengthlatticeslist-decodingmobility managementmultimodal modelsmutual correlated agreement error','beamforming','near-field','ssparse vector codingxURLLC
What happened
This document is an arXiv RSS snapshot (math/IT) containing multiple new papers (July 14, 2026) across wireless communications, coding theory, lattice algorithms, and signal processing. Highlights include an environment-aware mobility-management scheme using large multimodal models (LMMs) that builds channel-capacity maps (CCMs) from RGB-D sensing for proactive handovers; a channel-knowledge-map (CKM)–empowered finite-blocklength RSMA design for xURLLC in high-mobility autonomous driving; a novel SCMA-inspired sparse vector coding (SCMA-SVC) for enhanced URLLC with low-complexity decoding; and
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_math_it
- Record identifier
- 928c5ae904e047a4d77506fb3fe22771d190652c524ec56c78408a13f9e4f25d
- Enrichment time
- 2026-07-14T08:51:47Z
- AI-assisted enrichment
- Yes
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