Maximum Likelihood Detection in the Presence of Non-Gaussian Jamming
Bibliographic record
Abstract
We consider a scenario in which a transmitter sends complex symbols drawn from multi-dimensional constellations to a receiver in the presence of a jammer emitting proactively and continuously a zero-mean complex Gaussian signal over an unknown complex Gaussian channel. The complex Gaussian signal transmitted over the unknown complex Gaussian channel induces a non-Gaussian signal at the receiver. For this scenario, we develop the optimal maximum likelihood (ML) detector for cases corresponding to whether the receiver has full channel state information (CSI), full channel distribution information (CDI), or partial CDI about the transmitter channel. The jammer CDI is assumed to be either partially or fully available at the receiver. Using the derived likelihood expressions, we identify cases in which the non-Gaussian signals resulting from the jammer's transmission can be approximated by Gaussian signals without affecting the efficacy of the ML detector. In these cases, we show, analytically and numerically, that the exact and Gaussian approximation detectors are equivalent, but the ML detector based on the Gaussian approximation is computationally superior to its exact counterpart. Furthermore, we identify cases in which the Gaussian approximation ML detector is not equivalent to the exact ML detector. In these case, our numerical results suggest that the advantage of the exact ML detector over the Gaussian approximation one can be significant.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".