Generalized iterative water-filling for MIMO MAC with mixed constraints (invited)
Bibliographic record
Abstract
Based on the efficient Generalized Water-Filling with Group peak Power constraints (GWFGP), an iterative algorithm to compute the optimal solutions to system throughput (sum-rate) maximization problems for the multi-user multiple input multiple output multiple access channels (MU-MIMO MAC) in the general communication systems or networks is proposed. The proposed iterative GWFGP algorithm (IGWFGP) has two layers of loops. An 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 for the sum rate maximization problems. The used convergence theory of IGWFGP and the algorithm of GWFGP are efficient and novel. To the authors' best knowledge, no algorithms have been reported in the open literature to solve the target problems of this paper. In addition, the proposed algorithm never requires to choose the initial point for iteration, unlike popular algorithms, including the interior point method. This point may avoid the burden to set up an initial point, especially as the system becomes more and more advanced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".