Robust equalisation for inter symbol interference communication channels
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Bibliographic record
Abstract
The problem of equalisation for communication channels with inter symbol interference (ISI) is investigated in this study. One practical yet challenging constraint for a channel with high transmission rate is incorporated into the modelling of the equalisation system: the communication channel is subject to uncertainties, which are assumed to be within a polytope with finite vertices. By using the augmentation method, the filtering error system of the equalisation problem is also characterised as a system with polytopic uncertainties. Sufficient conditions on the stability and the ℋ∞ performance for the filtering error system are obtained. A design method for the equaliser is proposed such that the filtering error system can achieve minimal ℋ∞ performance index even with the channel uncertainties. Two illustrative design examples demonstrate the design procedure and the effectiveness of the proposed method.
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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 | 0.001 |
| 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 it