Missing Data Inference for Crowdsourced Radio Map Construction: An Adversarial Auto-Encoder Method
Bibliographic record
Abstract
Radio environment monitoring is crucial for many network engineering applications. Integrated with mobile crowdsourcing (MCS), radio map can be updated by mobile users in a low-cost manner. However, the crowdsourced measurement data may get quite sparse, and contain noises and errors. Therefore, how to efficiently infer missing data under low-quality measurements is critical in crowdsourced radio map construction. Existing inference methods like matrix completion require certain strict conditions, e.g. missing at completely random (MACR), which is impractical in the city-scale sensing. To address these issues, we propose a deep learning scheme based on adversarial auto-encoder (AAE) to handle measurements with large missing regions and complicated loss patterns. Specifically, this scheme applies variational auto-encoder (VAE) to infer missing data, and further utilizes the adversarial nets to play a min-max game with the VAE to improve recovery quality. Comprehensive experiments on three real datasets show that the proposed scheme can outperform state-of-the-art methods under large missing rates and low-quality measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".