Multiple-symbol differential decision fusion for mobile wireless sensor networks
Bibliographic record
Abstract
We consider the problem of decision fusion in mobile wireless sensor networks where the channels between the sensors and the fusion center are time-varying. We assume that the sensors make independent local decisions on the M hypotheses under test and report these decisions to the fusion center using differential phase-shift keying (DPSK), so as to avoid the channel estimation overhead entailed by coherent decision fusion. For this setup we derive the optimal and three low-complexity, suboptimal fusion rules which do not require knowledge of the instantaneous fading gains. The suboptimal fusion rules are obtained by applying certain approximations to the optimal fusion rule and are referred to as Chair-Varshney (CV), ideal local sensors (ILS), and max-log fusion rules. Since all proposed fusion rules exploit an observation window of at least two symbol intervals, we refer to them collectively as multiple-symbol differential (MSD) fusion rules. For binary hypothesis testing, we derive performance bounds for the optimal fusion rule and exact or approximate analytical expressions for the probabilities of false alarm and detection for all three suboptimal fusion rules. Simulation and analytical results show that whereas the CV and ILS fusion rules approach the performance of the optimal fusion rule for high and low channel signal-to-noise ratios (SNRs), respectively, the max-log fusion rule performs close-to-optimal for the entire range of SNRs. Furthermore, in fast fading channels significant performance gains can be achieved for the considered MSD fusion rules by increasing the observation window to more than two symbol intervals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".