Blind Channel Equalization Using MCMA Algorithm with Adam Optimization
Bibliographic record
Abstract
This paper introduces Adam-MCMA, combining the Modified Constant Modulus Algorithm (MCMA) with Adam optimization for faster blind channel equalization. Leveraging Adam's adaptive updates, our approach significantly accelerates convergence compared to traditional methods. Simulation results for 16-QAM show Adam-MCMA achieved a lower final MSE (18.91 dB) than standard MCMA (-17.57 dB) with approximately$\mathbf{9 8. 6 \%}$fewer iterations and$\mathbf{9 6. 4 \%}$less processing time. While offering performance comparable to Multi Modulus Algorithm (MMA) ($\mathbf{- 1 9. 9 5 ~ d B), ~ A d a m - M C M A ~ c o n v e r g e s ~ o r d e r s ~ o f ~ m a g - ~}$nitude faster. This demonstrates Adam-MCMA's computational efficiency, making it highly suitable for applications requiring rapid equalization of high-order constellations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".