Extrinsic Versus App Information Feedback in Turbo Vep Mu-Mimo Receivers: Optimization Via Deep Unfolding.
Bibliographic record
Abstract
The joint use of Soft-Input Soft-Output (SISO) detectors and channel decoders in an iterative manner has received growing attention for Multi-User Multiple-Input Multiple-Output (MU-MIMO) transmission schemes since several years, as it has been shown to operate close to fundamental limits, at least asymptotically. Amongst SISO detectors, message passing algorithms such as Vector Expectation Propagation (VEP) proved to outperform significantly linear detectors such as Linear Minimum Mean Square Error (LMMSE). Aside from its higher computational complexity, turbo VEP receivers rely on different hyper-parameters that can be optimized.In this context, we propose a joint optimization through deep-unfolding of the hyper-parameters that naturally arise in this kind of doubly iterative turbo VEP receivers. One of the difficulties arising for this type of receiver is when and how to choose between an extrinsic or an A Posteriori (APP) information feedback within the turbo receiver. The optimal selection is shown here to depend on the type of the considered SISO components. By properly choosing the hyper-parameters to be optimized, we show that deep-unfolding can naturally optimize the trade-off between extrinsic and APP information feedback and bring performance gains.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".