Improved Sum Power Iterative Water-Filling with Rapid Convergence and Robustness for Multi-Antenna Gaussian Broadcast Channels
Bibliographic record
Abstract
In our previous works, we have pointed out that when the iterative water-filling algorithms are applied to find the sum capacity of the multi-input multi-output broadcast channel (MIMO BC), it has some limitations. One of the major limitations is that the greater the number of the users becomes, the slower convergence of the iterative water-filling algorithms appear to be. To address this limitation, this paper first presents a new iterative water-filling algorithm for the MIMO BC. When compared with previous research, as the first advantage, the performance of this proposed algorithm has a strong robustness for the number of the users K; as the second advantage, parallel processing can be utilized to benefit the speed of computation during the implementation of this proposed algorithm. In addition, the proposed new algorithm is effective for finding the optimal transmission 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 | 0.000 |
| 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".