Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction
Year: 2024
Type: article
Institution: University of Minnesota
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: closed
Funder: National Science Foundation
Awards: Hybrid mmWave mMIMO Transceiver Design for Doubly-Selective Channels, CCSS: Online Learning for IoT Monitoring and Management, IMR: MM-1C: Learning-driven Models for 5G Internet Measurements, Collaborative Research: CIF: Medium: Robust Learning over Graphs, CIF: III: Medium: MoDL+: Analytical Foundations for Deep Learning and Inference over Graphs, CPS: Medium: Collaborative Research: Collective Intelligence for Proactive Autonomous Driving (CI-PAD)
OpenAlex ID: W4403722506