A Block Index Modulation Aided Multiple Sequences Spread-Spectrum System for Underwater Acoustic Communication
Bibliographic record
Abstract
Multiple sequences spread-spectrum (MSSS) systems have the characteristic of transmitting pilot sequence and data sequences simultaneously, making them more channel adaptable than traditional spread-spectrum communication systems. In this paper, we propose a new L-based block index modulation system based on the MSSS system (MSSS-B-IM), which significantly boosts the data rate. This scheme divides all possible index ranges of the spreading sequences into$J$groups, each group including at least ($L+1$) indexes. In each group, the information bits are mapped to the index to select one circularly shifted spreading sequence in one of the$L$indexes to complete code index modulation. The pilot signal is also spread by the original spreading sequence$\mathbf{c}$and then superimposed with the information symbols in the time domain at the transmitter. By analyzing and comparing the data rate of the MSSS, MSSS-IM and MSSS-B-IM systems, the proposed system shows significant improvement in the data rate under the same resource restrictions. In addition, the performance of the proposed scheme is evaluated through Monte Carlo simulation and sea trial, the results verify the superiority of the proposed scheme.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".