Joint decoding and data fusion in wireless sensor networks using turbo codes
Bibliographic record
Abstract
We consider the problem of joint decoding and data-fusion in data gathering sensor networks modeled by the Chief Executive Officer (CEO) problem. Correlation between sensorspsila data is known at the fusion center and is employed to update extrinsic information received from soft-in soft-out (SISO) decoders. It is shown in the literature that this scheme has a lower bit error rate compared with the schemes that separately decode data received from each sensor and then estimate the value of the source. Previous works consider correlated Gaussian sources and apply a single SISO decoder. We consider the binary CEO problem, where all sensors observe the same binary source corrupted by independent binary noises, and apply turbo codes to encode and transmit them to the fusion center. We show how extrinsic information is passed between SISO decoders and the vertical-decoding unit that updates extrinsic information using channel correlations. We illustrate the performance of the joint decoder for different correlations and rates. Simulation results show promising improvements compared with the separate decoding scheme. We also compare the bit error rates achieved by turbo codes with the ones achieved by convolutional codes and discuss the results.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".