Bibliographic record
Abstract
Spectrum optimization is an important part of the design of interference-limited multiuser communication systems. While traditional water-filling provides a closed-form solution to the transmit optimization problem in a single-user system, the multiuser version of the problem often leads to nonconvex problem formulations which are difficult to solve. Motivated by high-speed parallel-link and digital subscriber line applications, this paper investigates two practical multiuser settings in which global or local optimal solutions to the multiuser spectrum optimization problems can be found efficiently. The first part of this paper considers a high-speed transmission system in which practical (but suboptimal) minimum-mean-squared-error linear equalizers (MMSE-LEs) are used at the receiver. The optimal single-user transmit spectrum in this case involves a modified water-filling solution. Surprisingly, such a modified water-filling spectrum can be shown to be near-optimal in a multiuser setting as well, if the direct-link and the crosstalk characteristics are symmetric and if crosstalk is reasonably small. Thus, for practical parallel-link systems using MMSE linear equalizers, the optimal single-user and multiuser spectra are nearly identical. The second part of this paper considers numerical techniques for solving a nonconvex multiuser rate maximization problem for digital subscriber line applications. A new ingredient in the proposed approach is a taxation scheme that takes into account the effect of interference between neighboring lines. This leads to a modified iterative water-filling algorithm which is capable of finding local optimum solutions to the multiuser spectrum optimization problem efficiently.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".