Power Allocation and Admission Control in Multiuser Relay Networks via Convex Programming: Centralized and Distributed Schemes
Bibliographic record
Abstract
The power allocation problem for multiuser wireless networks is considered under the assumption of amplify-and-forward cooperative diversity. Specifically, optimal centralized and distributed power allocation strategies with and without minimum rate requirements are proposed. We make the following contributions. First, power allocation strategies are developed to maximize either (i) the minimum rate among all users or (ii) the weighted-sum of rates. These two strategies achieve different throughput and fairness tradeoffs which can be chosen by network operators depending on their offering services. Second, the distributed implementation of the weighted-sum of rates maximization-based power allocation is proposed. Third, we consider the case when the requesting users have minimum rate requirements, which may not be all satisfied due to the limited-power relays. Consequently, admission control is needed to select the number of users for further optimal power allocation. As such a joint optimal admission control and power allocation problem is combinatorially hard, a heuristic-based suboptimal algorithm with significantly reduced complexity and remarkably good performance is developed. Numerical results demonstrate the effectiveness of the proposed approaches and reveal interesting throughput-fairness tradeoff in resource allocation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".