Score-Based Generative Modeling for MIMO Detection Without Knowledge of Noise Statistics
Bibliographic record
Abstract
Motivated by recent advances in deep generative probabilistic modelling, we propose a robust multiple-input multiple-output (MIMO) symbol detector that aims to perform maximum likelihood (ML) detection without knowledge of the noise statistics. While the optimal MIMO detector (under uniform priors) is the ML detector, its implementation requires knowledge of the noise distribution. Furthermore, for some types of additive noise such as impulsive noise, the probability density function (PDF) of the noise does not admit a closed form expression thus making ML detection intractable. To overcome these limitations, our proposed approach learns a score function of the noise distribution directly from data. Subsequently, the learned score function is used to transform the noise distribution to a known (and tractable) prior distribution through the use of a stochastic differential equation. Via numerical simulations, the proposed detector is shown to outperform recent benchmark approaches for various types of additive noise, and to achieve near optimal ML performance where applicable.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".