Relay assignment in multiple source-destination cooperative networks with limited feedback
Bibliographic record
Abstract
We consider in this paper relay assignment for cooperative systems with mutiple source-destination pairs. The objective here is to assign the relays to the source-destination pairs in a such way that all pairs would achieve the maximum diversity. Normally, for a network with mutiple source-destination pairs, none of the destinations can acquire the channel state information (CSI) of the entire network without feedback. To this end, we design a practical limited feedback strategy in conjunction with two relay assignment schemes, i.e., fullset selection and subset selection, which are based on maximizing the minimum end to end (E2E) signal to noise ratio (SNR) among all pairs. In this strategy, each destination acquires its signal-to-noise ratios (SNRs), quantizes them, and feeds them back to the relays. The relays construct the end-to-end (E2E) SNR matrix and select the relay assignment choice from all possible relay assignment permutations or only a subset of these permutations. We analyze the performance of these schemes over independent Rayleigh fading channels in terms of the worst E2E SNR. We investigate the asymptotic performance of the proposed schemes at high SNR. We show that relay assignment with quantized CSI can achieves the same first-order diversity as that of full CSI, but there is a second-order diversity loss. We also demonstrate that increasing the quantization levels yields performance that is close to that of having full knowledge of the CSI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".