Jointly Optimal Power and Resource Allocation for Orthogonal NDF Relay Systems with QoS Constraints
Bibliographic record
Abstract
One of the advantages of relaying is that it offers the potential for a reduction the power required to achieve a specified level of quality-of-service (QoS). However, the problem of optimizing the available resources so as to minimize this power is often difficult to solve. In this paper we consider the case of a point-to-point link assisted by an orthogonal non-regenerative decode-and-forward (NDF) relay that is allocated a fraction of the time block. We assign prices to the powers of the source and the relay, and we consider the problem of jointly optimizing the source power, the relay power and the fraction of the time block so as to minimize the total cost of the power required to achieve a specified target rate. The natural formulation of that problem is not convex, but by analyzing the structure of the constraints we obtain a quasi-closed-form expression for the optimal solution that, at most, requires the solution of a simple one-dimensional zero-crossing problem for a monotonic function. This enables the problem to be efficiently solved, and clearly identifies when relaying is superior to direct transmission. Our numerical results illustrate the extent of the gains over regenerative decode-and-forward relaying, in which the resource allocation is, by definition, constrained to be equal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".