Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization
This work addresses uncertainty quantification in wireless systems for planning and control, but it appears incremental as it builds on existing posterior inference methods with a novel neural approach.
The paper tackles the problem of wireless localization by formulating it as posterior inference of an unknown transmitter location from receiver measurements, proposing MC-CLE, which achieves lower cross-entropy loss relative to baselines like uniform and Gaussian posteriors in simulations.
Modern wireless systems require not only position estimates, but also quantified uncertainty to support planning, control, and radio resource management. We formulate localization as posterior inference of an unknown transmitter location from receiver measurements. We propose Monte Carlo Candidate-Likelihood Estimation (MC-CLE), which trains a neural scoring network using Monte Carlo sampling to compare true and candidate transmitter locations. We show that in line-of-sight simulations with a multi-antenna receiver, MC-CLE learns critical properties including angular ambiguity and front-to-back antenna patterns. MC-CLE also achieves lower cross-entropy loss relative to a uniform baseline and Gaussian posteriors. alternatives under a uniform-loss metric.