Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910016015958016 |
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| author | Lei, Haozhe Guo, Hao Svensson, Tommy Rangan, Sundeep |
| author_facet | Lei, Haozhe Guo, Hao Svensson, Tommy Rangan, Sundeep |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization Lei, Haozhe Guo, Hao Svensson, Tommy Rangan, Sundeep Signal Processing Information Theory Machine Learning 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. |
| title | Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2509.25719 |