Methods › General › Position Recovery Models › PRNet+
PRNet+
Introduced by Yige Zhang et al. in Outdoor Position Recovery from HeterogeneousTelco Cellular Data
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
PRNet+ is a multi-task neural network for outdoor position recovery from measurement record (MR) data. PRNet+ develops a feature extraction module to learn common local-, short- and long-term spatio-temporal locality from heterogeneous MR samples, with a convolutional neural network (CNN), long short-term memory cells (LSTM), and attention mechanisms. Specifically, PRNet+ 1) allows the various-length sequences of MR samples, such that the two components (CNN and LSTM) are able to capture spatial locality from the samples within each MR sequence, 2) exploits two attention mechanisms for the time-interval between neighbouring MR samples, together with the one between neighbouring MR sequences, to capture temporal locality, and 3) incorporates the detected transportation modes and predicted locations of heterogeneous MR data into a joint loss for better result.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Outdoor Position Recovery from HeterogeneousTelco Cellular Data 24 Aug 2021 · 0 repositories · arXiv:2108.10613
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Multi-Task Learning | 1 |
| Position | 1 |
| Transportation Mode Detection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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