Transmitter identification using embedded spread spectrum sequences
Bibliographic record
Abstract
A transmitter identification system using embedded spread spectrum sequence is proposed in this paper. Principles of the proposed technique is illustrated through an example of digital TV (DTV) transmitters identification. Code generators in Matlab are developed to study the auto-correlation and cross-correlation properties of the Gold and Kasami sequences. Spread spectrum sequences with different periods are generated and simulated. To speed-up the identification process, the embedded spread spectrum sequence is preferred to be time-synchronized with the DTV signal. By doing so, code length has to be truncated before it is fitted into each field of the ATSC DTV signal. The impact of truncation noise and in-band DTV interference on transmitter identification is also investigated in this paper. It is shown that the auto-correlation and cross-correlation properties are only slightly affected by truncation noise. It is also shown that the dominant interference to the transmitter identification is the in-band DTV signal. The signal to truncation noise ratio and signal to DTV interference ratio in the correlation output are derived and verified form the simulation results. It is further recognized that in-band DTV interference can only be mitigated by increasing the code length or by time-domain averaging technique to smoothen out the in-band interference.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".