Generation of balanced quadrature phase shift keyed sequences through guided scrambling
Bibliographic record
Abstract
Balanced codes (also called DC‐free codes) are widely used in binary communication systems to increase the likelihood of accurate symbol recovery with practical demodulators. Guided scrambling (GS) is recognised as a viable approach to efficiently generate DC‐free binary sequences. In this study the authors extend the use of GS to generate balanced sequences of quadrature phase shift keyed (QPSK) symbols by using arithmetic from the ring of polynomials defined over the Galois field of four elements. In addition to ensuring adequate timing information and consistent decision thresholds to improve the performance of practical demodulators, balanced encoding of QPSK symbol sequences creates a null at DC in the spectrum of the equivalent complex baseband signal. This corresponds to a null at the centre frequency of the bandpass QPSK signal that enables the insertion of a pilot tone and avoidance of narrowband interference without filtering or distortion of the signal. The authors outline sufficient conditions for the generation of balanced GS QPSK sequences, and based upon these conditions the authors recommend scrambling polynomials and quotient selection criteria. The authors then present analytical and simulation results that confirm the generation of balanced sequences using this approach.
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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 | 0.000 | 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".