Further Designs of Low-Hit-Zone Frequency-Hopping Sequence Set with Strictly Optimal Partial Hamming Correlation
Bibliographic record
Abstract
The quasi-synchronous frequency-hopping (FH) multiple access (QS-FHMA) communication system has the advantages of not requiring precise time synchronization, low equipment complexity, and easy to implement in engineering applications. And it is widely applied in military communication systems, vehicle-to-everything (V2X) communication, satellite communication systems, and industrial internet of things (IIoT), among others. FH sequences set with Low-Hit-Zone (LHZ), in LHZ both the Hamming autocorrelation of each sequence and the Hamming cross-correlation between distinct sequences remain low, are an important component of QS-FHMA communication system. The utilization of LHZ FH sequence set (LHZ FHS set) with optimal partial Hamming correlation (PHC) can effectively enhance the communication performance of QS-FHMA system. Based on q-ary m-sequence with degree n and its decimated sequence, q ≠ 2, n ≥ 2, this paper constructs three types of LHZ FHS sets. Experimental results show that these sequence sets have strictly optimal periodic PHC property, good wide gap property, and favorable complexity. The new constructions can provide more high-performance LHZ FHS sets for QS-FHMA communication systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".