πArXiv AIβ’Stalecollected in 23h
LOREN: Low-Rank Adaptation for Neural Receivers
β‘ 30-Second TL;DR
What Changed
Low-rank adapters in convolutional layers
Why It Matters
Makes neural receivers practical for wireless systems by reducing memory and power needs. Supports multiple code rates efficiently in 22nm tech.
What To Do Next
Evaluate benchmark claims against your own use cases before adoption.
Who should care:Researchers & Academics
Key Points
- β’Low-rank adapters in convolutional layers
- β’65% silicon area savings, 15% power reduction
- β’End-to-end training on 3GPP channels
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Original source: ArXiv AI β
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