# Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization

arXiv: https://arxiv.org/abs/2509.25719 · HTML: https://mlsanity.com/paper/2509.25719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [LinCDE: Conditional Density Estimation via Lindsey's Method](https://mlsanity.com/paper/2107.12713/) (2021)
- [Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2306.06766/) (2023)
- [Cellular Wireless Networks in the Upper Mid-Band](https://mlsanity.com/paper/2309.03038/) (2023)
- [Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigation](https://mlsanity.com/paper/2506.22365/) (2025)
- [Uncertainty Awareness in Wireless Communications and Sensing](https://mlsanity.com/paper/2412.14369/) (2024)
- [3D angle-of-arrival positioning using von Mises-Fisher distribution](https://mlsanity.com/paper/1709.02437/) (2017)

## Built on by (most cited first, 1 of 1)

- [LOCUS-DT: Localization via Observation-Conditioned Uncertainty Scoring with Digital Twins](https://mlsanity.com/paper/2608.00406/) (2026)

## Abstract (arXiv)

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.