Bibliographic record
Abstract
The open nature of the wireless channel makes it vulnerable to many security attacks like jamming and eavesdropping.Jamming threatens the existence of wireless services and can lead to denial of service.Barrage jamming where the jammer emits white Gaussian noise that occupies the entire transmission bandwidth is known to be the most harmful when the jammer has no knowledge about the target system or when it targets many different systems.The majority of literature models the barrage jamming signal at the receiver as additive Gaussian noise.The accuracy of this model in mobile communication scenarios is questionable.The complex Gaussian signal transmitted over the unknown complex Gaussian channel induces a non-Gaussian signal at the receiver.Knowing the distribution of the received jamming signal is fundamental to develop detectors and to compute the probability of detection error.This thesis considers scenarios where a single-antenna transmitter sends complex symbols drawn from one-dimensional or multi-dimensional constellation to a receiver equipped with a single, double, or multiple antennas in the presence of a singleantenna barrage jammer.The exact likelihood expressions of the received signal and the likelihood expressions based on the Gaussian approximation of the signal induced by the jammer's transmissions are derived for scenarios in which 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, two maximum likelihood (ML) detectors are developed for each scenario.One detector is based on the exact likelihood expressions and the other is based on the Gaussian approximation expressions.The performance of each detector is investigated and cases in which the two detector are equivalent are identified analytically and experimentally.Furthermore, the effects of the number of receive antennas, and symbol length on the detection performance are investigated.iii xiv
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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 | 0.009 |
| 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 | 0.002 | 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".