Maximum Likelihood Detection in Single-Input Double-Output Non-Gaussian Barrage-Jammed Systems
Bibliographic record
Abstract
We derive the likelihood functions and the maximum likelihood (ML) detectors for four classes of single-input double-output (SIDO) communication systems, i.e., systems with one transmit and two receive antennas. For all classes, the received signals are contaminated by a Gaussian noise component and a non-Gaussian component induced by the Gaussian transmissions of a proactive continuous single-antenna jammer over an unknown complex$2\times 1$Gaussian vector channel. The considered classes correspond to whether full channel distribution information (CDI), or partial CDI about the transmitter channel and the jammer channel is available at the receiver. Unlike their scalar counterparts, the vector channels considered herein interweave the components of the received signal, rendering the derivation of the likelihood function a daunting task for more than two receive antennas. Furthermore, the interweaving of the received signal components in the vector channel case prevents the optimal ML detector for unit-norm constellations from reducing to the corresponding Gaussian approximation-based detector. This is in sharp contrast with the scalar case, wherein the two detectors are equivalent for unit-norm constellations. Confirming our analytical findings, experimental results show that the difference between the two detectors can be significant, especially when the transmitter-receiver and jammer-receiver channels have substantial line-of-sight components. Although the computational cost of performing optimal ML detection in the presence of non-Gaussian jamming is higher in the case of two receive antennas, the performance advantage over the single antenna case justifies it.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".