Distributed Channel-Aware Quantization Based on Maximum Mutual Information
Bibliographic record
Abstract
In distributed sensing systems with constrained communication capabilities, sensors' noisy measurements must be quantized locally before transmitting to the fusion centre. When the same parameter is observed by a number of sensors, the local quantization rules must be jointly designed to optimize a global objective function. In this work we jointly design the local quantizers by maximizing the mutual information as the optimization criterion, so that the quantized measurements carry the most information about the unknown parameter. A low-complexity iterative approach is suggested for finding the local quantization rules. Using the mutual information as the design criterion, we can easily integrate the effect of communication channels in the design and consequently design channel-aware quantization rules. We observe that the optimal design depends on both the measurement and channel noises. Moreover, our algorithm can be used to design quantizers that can be deployed in different applications. We demonstrate the success of our technique through simulating estimation and detection applications, where our method achieves estimation and detection errors as low as when designing for those special purposes.
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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 | 0.006 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".