Experimental carrier detection of BPSK and QPSK direct sequence spread spectrum signals
Bibliographic record
Abstract
This paper reports on an experimental investigation of the detection of BPSK and QPSK direct sequence (DS) spread spectrum signals above and below the noise floor using nonlinear carrier detection methods. These processes produce carrier harmonics 2f/sub 0/ and 4f/sub 0/ for BPSK and QPSK signals respectively. The signal-to-noise ratio of these harmonics depends on the input signal signal-to-noise ratio, SNR (chip symbol energy/noise power density), normalized input bandwidth, /spl eta/(input filter bandwidth/chip rate) and interceptor's process gain, R/B (chip rate/detection bandwidth). Measurements agree well with analytic expressions. At low input SNR, the signal-to-noise ratio of the carrier harmonics is maximized for /spl eta/=1.0 and varies as SNR/sup 2/ for BPSK signals and in the range of SNR/sup 3/ to SNR/sup 4/ for QPSK signals (the theoretical limit /spl prop/SNR/sup 4/ is only sometimes reached in practice.) QPSK signals generated from typical commercial quadrature modulators may also be detected at If/sub 0/ from mixer LO leak-through and at 2f/sub 0/ from I/Q channel imbalance. For LPI use of QPSK DS signals, the LO leak-through must be carefully suppressed to <-55 dB and the two channels balanced within 0.2 dB. For a typical R/B=60 dB (10 MHz chip rate detected in a 10 Hz detection bandwidth), the threshold of detection is an SNR of -23 dB for BPSK signals and -8 dB for QPSK signals.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".