Bibliographic record
Abstract
The purpose of this paper is to present the impact of intentional electronic countermeasures using a high-speed repeater jammer on a single input single output (SISO) OFDM communication system. The jammer is composed of high-speed ADCs, DACs and a high performance FPGA used to implement a digital radio frequency memory (DRFM) and other jammers. This jammer can repeat a modified version of the input signal by continuously changing the amplitude or delay. The DRFM method can then be used to generate advanced jamming techniques capable of replicating fast fading channels similar to multipath environments. It can also add a continuous wave, broadband or partial band noise. In this study, WiMAX signals are subjected to these different types of jamming. The resulting EVM measures are converted using simple probability equations relating EVM to BER. The signal's immunity to jamming can be extracted by measuring the needed Signal-to-Interference-Ratio (SIR) required to reach a predetermined BER threshold. The most effective jammers are then determined. Power-to-power comparisons show that the best intelligent jammer is only 2 dB less efficient compared to an ideal "genie-aided" noise jammer which knows which band to target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".