Improved Iterative Water-Filling with Rapid Convergence and Parallel Computation for Gaussian Multiple Access Channels
Bibliographic record
Abstract
For a class of the important problems that seek to maximize the sum rate of the multi-user multiple input multiple output multiple access channel (MIMO MAC) and compute the corresponding optimal input distribution, we developed a more efficient algorithm for solving this class of the problems compared with currently known algorithms. The performance result of this new algorithm indicates that the proposed algorithm overcomes some of the weaknesses of other algorithms. One of the key weaknesses that it overcomes is that the well-known iterative water-filling algorithms cannot utilize the machinery of parallel computation, owning to their inherent structure defects. Not only does the proposed algorithm sufficiently utilizes the machinery of parallel computation, it also shows faster convergence compared with previous research results. Numerical results show that the same properties of the proposed algorithm are also effective for finding the optimal input policy due to its simplicity and fast convergence.
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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 | 0.000 | 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".