One-Bit Feedback Selection Schemes for Power-Efficient Multiuser and Multirelay Systems
Bibliographic record
Abstract
This paper proposes new selection schemes based on one-bit feedback for a system with multiple amplify-and-forward relays and users over Rayleigh fading channels. We introduce new distributed selection (DS), simplified DS (DSS), and centralized selection (CS) schemes that reduce outage and save transmit power using one-bit feedback. The CS scheme is optimal by selecting only the dual-hop link with equivalent end-to-end SNR above the outage threshold. DS and DSS schemes, on the other hand, require less feedback at the BS and employ a decentralized selection policy. The feedback bit in those schemes render the comparison result between the first- and second- hop SNR and an appropriate threshold. The chosen selection threshold in DS and DSS schemes allows the dual-hop links for which the system outage is certain to be discarded. In the three selection schemes, transmission is switched off to save power when there is no dual-hop link meeting the selection requirement. Closed-form expressions for the DS, DSS, and CS outage probabilities are derived. Numerical results reveal that, even though DSS is a simplified version of the DS scheme, they have similar outage performance. Additionally, DS and DSS are quite competitive with CS for a small number of relays relative to the number of users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".