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Graph-guided semi-supervised learning (SSL) has gained popularity in several network science applications, including biological, social, and financial ones. SSL becomes particularly challenging when the available nodal labels are scarce, what motivates naturally the active learning (AL) paradigm. AL seeks the most informative nodes to label in order to effectively estimate the nodal values of unobserved nodes. It is also referred to as active sampling, and boils down to learning the sought function mapping, and an acquisition function (AF) to identify the next node(s) to sample. To learn the mapping, this work leverages an adaptive Bayesian model comprising an ensemble (E) of Gaussian Processes (GPs) with enhanced expressiveness of the function space. Unlike most alternatives, the EGP model relies only on the one-hop connectivity of each node. Capitalizing on this EGP model, a suite of novel and intuitive AFs are developed to guide the active sampling process. These AFs are then combined with weights that are adapted incrementally to further robustify performance. Numerical tests on real and synthetic datasets corroborate the merits of the novel methods.
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Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction
The advent of diverse frequency bands in 5G networks has promoted measurement studies focused on 5G signal propagation, aiming to understand its pathloss, coverage, and channel quality characteristics. Nonetheless, conducting a thorough 5G measurement campaign is markedly laborious given the large number of 5G measurement samples that must be collected. To alleviate this burden, the present contribution leverages principled active learning (AL) methods to prudently select only a few, yet most informative locations to collect 5G measurements. The core idea is to rely on a Gaussian Process (GP) model to efficiently extrapolate 5G measurements throughout the coverage area. Specifically, an ensemble (E) of GP models is adopted that not only provides a rich learning function space, but also quantifies uncertainty, and can offer accurate predictions. Building on this EGP model, a suite of acquisition functions (AFs) are advocated to query new locations on-the-fly. To account for realistic 5G measurement campaigns, the proposed AFs are augmented with a novel distance-based AL rule that selects informative samples, while penalizing queries at long distances. Numerical tests on 5G data generated by the Sionna simulator and on real urban and suburban datasets, showcase the merits of the novel EGP-AL approaches.
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- Award ID(s):
- 2212318
- PAR ID:
- 10518965
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE transactions on wireless communications
- ISSN:
- 1536-1276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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