Margin maximization in multiuser interference digital subscriber line channels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiuser margin maximization algorithms are developed for multi-carrier digital subscriber loops (DSL) employing dynamic spectrum management (DSM). Margin maximization is desirable for constant bit rate applications and provides protection against various non-stationary and bursty noise sources. Most single-user margin maximization algorithms rely on a fixed crosstalk assumption, which does not hold in DSM. Thus with direct extension of single-user algorithms in DSM scenarios, one user's margin maximization can lead to the failure of other users in meeting their target rates. In this paper, we explore the favorable monotonicity and fairness properties in multiuser margin and use them to formulate a box-constrained non-linear least squares (NLSQ) problem that can be solved by using a scaled gradient trust region approach with Broyden Jacobian update. This algorithm efficiently converges to a solution providing the best common equal margin to all users while explicitly guaranteeing that each user's target rate requirement is satisfied. The algorithm can be implemented in practical DSL-DSM scenarios with only Level-1 coordination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it