Joint optimal power allocation and relay selection with spatial diversity in wireless relay networks
Bibliographic record
Abstract
Abstract—We consider a wireless relay network (WRN) where multiple mobile stations (MSs) try to send their data to a base station (BS) either directly or via a set of fixed relay stations (RSs). For this network, we study the problem of joint optimal MS and RS power allocation and relay selection with the objective of minimizing the total transmitted power of the system. The joint optimization algorithm must satisfy the minimum data demand of each MS. We formulate the problem as a mixed integer nonlinear programming (MINLP) problem and find the solution under different relaying architectures and spatial diversity schemes. The optimal solution of the MINLP problem is exponentially complex due to its combinatorial nature. We use the MATLAB based commercial software TOMLAB to find a near optimal solution of the MINLP problem. We also find an approximate solution of the original problem by applying a simple relay selection scheme based on the channel gains between MSs and RSs. Numerical results are presented to show the performance of this simple scheme with respect to the optimal one in terms of total power consumption. I.
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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 | 0.000 | 0.001 |
| 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".