Optimal Power Allocation for Maximum Throughput of General MU-MIMO Multiple Access Channels With Mixed Constraints
Bibliographic record
Abstract
Based on the efficient generalized water-filling with group peak power constraints (GWFGP), this paper proposes an iterative algorithm to compute the optimal solutions to system throughput (sum-rate) maximization problems. This class of problems is equipped with the multiuser multiple input multiple output multiple access channels (MU-MIMO MAC) in the general communication systems. The proposed iterative GWFGP algorithm (IGWFGP) has two levels of loops. The inner loop aims at computing the solution to each member in the family, while the outer loop aims at computing the solution to the target problem based on the results obtained by the inner loop. Both GWFGP and the convergence theory of an algorithm are used in the inner loop and the outer loop respectively. Furthermore, by exploiting the concept of variable weighting factor for covariance update, IGWFGP owns fast convergence and provides optimal solutions to the sum rate maximization problems. The usage of the convergence theory in IGWFGP and the algorithm of GWFGP are efficient and novel. To the best of the authors' knowledge, no prior algorithm has been reported in the open literature to solve the targeted problem in this paper. In addition, the proposed algorithm does not require to choose the initial value for computation. This feature is a significant advantage of the algorithm, especially for large and complicated systems.
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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 | 0.001 |
| 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".