Signal Detection in Non-Gaussian Barrage-Jammed Multiple Antenna Systems Via Decision Fusion
Bibliographic record
Abstract
Jamming is an immense security threat. Barrage jammers transmitting Gaussian noise over Gaussian channels induce non-Gaussian noise at the receiver. Optimal maximum likelihood detection for such channels when the number of receive antennas is strictly greater than two, as in emerging 6G and beyond communication systems, is mathematically intractable. To circumvent this difficulty, we consider all possible antenna pairs. For each pair, we obtain the optimal maximum likelihood decision. We subsequently use fusion techniques to obtain final decisions. Three such techniques are considered, viz., simple majority, likelihood-weighted majority and entropy-weighted majority votes. Numerical simulations for both Rayleigh and Rician jammer channels show that using fusion techniques for receivers with more than two antennas offer significant performance gains over commonly-used detectors based on Gaussian approximations of the received signals. Moreover, simulation results show that using fusion techniques achieves almost full diversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".