Enhanced multiuser resource allocation using near-continuous loading algorithms with adaptive coding for digital subscriber loops
Bibliographic record
Abstract
In this paper, we present a multi-user resource allocation technique for multi-carrier digital subscriber loop (DSL) systems using an adaptive coding/modulation scheme. Traditional bit loading based on only adaptive modulation requires the number of bits to be integer and hence causes granularity loss that can be significant in dynamic spectrum management (DSM) when each user's sub-carrier-specific power is implicitly constrained due to other user's rate demand. By adaptively varying the code rate of a programmable Reed Solomon (RS) code, a fine-granularity loading in conjunction with the purely distributed iterated water filling (IWF) is proposed to recover such granularity losses. Simulation results for various VDSL-DMT scenarios show that the proposed scheme can provide higher rates than the distributed IWF and centralized optimum spectrum management (OSM) with integer-bit loading
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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".