BLINC uses large language models to guide Bayesian network causal learning for RAN parameter optimization, delivering 63.5% throughput gains and 19.7% block error rate reduction over data-only baselines in a private 5G testbed while enabling interpretable, adaptive models.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
BLINC: Context-Specific Causal Learning for Automated RAN Configuration
BLINC uses large language models to guide Bayesian network causal learning for RAN parameter optimization, delivering 63.5% throughput gains and 19.7% block error rate reduction over data-only baselines in a private 5G testbed while enabling interpretable, adaptive models.