arXiv (All)
open access
Meta-Transfer Learning for mmWave Beam Alignment
Millimeter-wave (mmWave) beam alignment is critical for next-generation wireless systems, but existing approaches either update all parameters during adaptation or restrict updates to a subset of layers without adapting intermediate feature representations. We propose MTL-BA, a meta-transfer learnin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels#798758
- Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels#797344
- RadioSight: Predictive mmWave XR Network Optimization from Dynamic Neural Radio Fields#790263
- SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations#1050297
- Adversarial Robustness of Near-Field Millimeter-Wave Imaging under Waveform-Domain Attacks#128208
Record · ID 1200991
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive