Threshold based distributed detection that achieves full diversity in wireless sensor networks
Bibliographic record
Abstract
It is well known that decode and forward relaying protocols, in which the relays decode and re-encode the message, can achieve full diversity when combined with distributed space time coding or relay selection schemes. However, in wireless sensor networks, decoding and re-encoding the message at the relays can be costly due to severe energy limitations. A question arises: is it possible to achieve full diversity when the relays only detect (or demodulate) and forward the message? In this paper a distributed detection scheme is developed that achieves full diversity in wireless sensor network, with the relays only detecting and forwarding the message. It is seen that such a relaying scheme is SNR threshold-based, i.e., the relays will perform detection only if the receive SNR is above a certain threshold. The threshold that achieves full diversity is derived. Furthermore, a single bit feedback scheme is developed to make relaying more spectral efficient, such that the network can achieve the diversity-multiplexing tradeoff of a point-to-point multiple-input single-output (MISO) system in the high SNR regime.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".