Joint Multiuser Detection and Optimal Spectrum Balancing for Digital Subscriber Lines
Bibliographic record
Abstract
In a digital subscriber line (DSL) system with strong crosstalk, the detection and cancellation of interference signals have the potential to improve the overall data rate performance. However, as DSL crosstalk channels are highly frequency selective and multiuser detection is suitable only when crosstalk is strong, the set of frequency tones in which multiuser detection may be used must be carefully chosen. Further, this problem of tone selection is highly coupled with the transmit power spectra of both direct and interfering signals, so the optimal solution requires the tone selection problem to be solved jointly with the multiuser spectrum optimization problem. The main idea of this paper is that the above joint optimization may be done efficiently using a dual decomposition technique similar to that of the optimal spectrum balancing algorithm. Simulations show that multiuser detection can increase the bit rate performance in a remotely deployed ADSL environment. Rate improvement is also observed when near-end crosstalk is estimated and cancelled in a VDSL environment with overlapping upstream and downstream frequency bands.
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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".