Low-complexity near-optimal spectrum balancing for digital subscriber lines
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the multiuser spectrum optimization problem for digital subscriber lines. We propose an iterative and low-complexity spectrum optimization technique that improves upon the recently proposed optimal spectrum balancing (OSB) algorithm. In the optimal spectrum balancing algorithm, the Lagrange multipliers are used to decouple the constrained optimization problem into a series of per-tone unconstrained optimisation problems. However, each per-tone problem still has a computational complexity that is exponential in the number of users. This paper proposes an iterative algorithm for the per-tone optimization problem to further reduce the computational complexity of spectrum balancing. The essential idea resembles that of iterative water-filling. In each step of the algorithm, each individual user iteratively optimizes the joint objective function with a fixed set of Lagrange multipliers. The new algorithm has a computational complexity that is polynomial in the number of users. Simulation results show that the new algorithm has a near-optimal performance.
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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 it