Power and Resource Allocation for Orthogonal Multiple Access Relay Systems
Bibliographic record
Abstract
We study the problem of joint power and channel resource allocation for orthogonal multiple access relay (MAR) systems in order to maximize the achievable rate region. Four relaying strategies are considered; namely, regenerative decode-and-forward (RDF), nonregenerative decode-and-forward (NDF), amplify-and-forward (AF), and compress-and-forward (CF). For RDF and NDF we show that the problem can be formulated as a quasiconvex problem, while for AF and CF we show that the problem can be made quasiconvex if the signal-to-noise ratios of the direct channels are at least -3dB. Therefore, efficient algorithms can be used to obtain the jointly optimal power and channel resource allocation. Furthermore, we show that the convex subproblems in those algorithms admit a closed-form solution. Our numerical results show that the joint allocation of power and the channel resource achieves significantly larger achievable rate regions than those achieved by power allocation alone with fixed channel resource allocation. We also demonstrate that assigning different relaying strategies to different users together with the joint allocation of power and the channel resources can further enlarge the achievable rate region.
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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 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".