Fine-granularity loading schemes using adaptive Reed-Solomon coding for discrete multitone modulation systems
Bibliographic record
Abstract
In this paper, we present a fine granularity loading scheme using joint optimization of modulation and coding for discrete multitone (DMT) modulation towards achieving maximum information rate conveyed. While most existing algorithms strived for the optimal energy distribution to maximize rate, the bits loaded were always constrained to integers. It was initially believed that most (not all) of the granularity losses could be recovered through 'bit-rounding' and 'energy re-scaling' after an optimal 'water-filling' approach. But this was observed only for the total power constrained case. With the advent of peak power constraint, we show that the room for optimization in the energy domain severely constrained and granularity losses constitute a significant percentage of the achievable data rate. To recover these losses, we propose a loading scheme that integrates the coding scheme with the bit-loading algorithm. For achieving near-continuous rate adaptation, the family of Reed-Solomon (RS) codes has been used for their low redundancy, high flexibility in correction capability and highly programmable architecture. Simulation results with very high bit rate digital subscriber line (VDSL)-DMT system show more than 20% improvement in most cases.
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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 | 0.001 |
| 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".