Adaptive Power Allocation for Bidirectional Amplify-and-Forward Multiple-Relay Multiple-User Networks
Bibliographic record
Abstract
Owing to its spectral efficiency, bidirectional relaying is a promising candidate for information exchange in multiple-user cooperative networks. When the network is limited by resource constraints, amplify-and-forward (AaF) relay protocol is often the choice due to its simplicity and ease of use. Power allocation for AaF protocol has being extensively studied in unidirectional relay networks but how it can be implemented in two-way multiple-relay multiple-user networks has yet to be addressed. In this paper, we consider the adaptive power allocation in bidirectional AaF multiple-relay multiple-user networks. We show that when the multiple-user interference can be removed by a robust channel assignment algorithm, power allocation by maximizing the instantaneous sum rate or minimizing the symbol error rate can be suitably casted as a geometric programming (GP) problem. Simulation results show adaptive power allocation by GP outperforms that of equal power allocation scheme particularly when there is a single serving relay, and the gain can be as substantial when there are multiple serving relays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".