A VLSI architecture of a piecewise RBF decision feedback channel equalizer
Bibliographic record
Abstract
This paper proposes a VLSI architecture for a decision feedback channel equalizer based on the Radial Basis Functions (RBF-DFE). The performance of this method approximates that of the optimal equalizer based on the maximum likelihood estimation (MLSE). The regularity of the operation structure makes possible a parallel implementation. The Gaussian characteristic of the RBF-DFE is approximated by a piecewise linear-function (PL-RBF-DFE) to propose a digital architecture. The PL-RBF-DFE equalizer was simulated and synthesized in CMOS 0.5 /spl mu/m technology. A throughput of 20 Mbits/s, over a surface occupied by 16,200 transistors (without memory area), was achieved for a parallel architecture. The proposed architectures are applicable in adaptive and blind channel equalization.
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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 | 0.001 | 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".