Two-way relay underwater acoustic communication channels with distributed space-time block coding
Bibliographic record
Abstract
In this paper we study the performance of two-way relaying (TWR) over underwater acoustic (UWA) channels in conjunction with distributed space-time block coding (D-STBC). In particular, we consider the communication between two sources via relay nodes. The underlying channels are characterized as doubly selective channels. Orthogonal frequency division multiplexing (OFDM) is used to combat frequency selectivity of the channels, while front-end multiple resampling (MR) combined with frequency-domain equalization is used to combat intercarrier interference (ICI) resulting from time selectivity of the channel caused by the relative motion between the transceivers. Simulation results show the superiority of MR over its single resampling (SR) counterpart. Also, under total power constraint, AF-D-STBC (when only one source is activated at a time) outperforms AF-TWR-D-STBC, however, at the expense of less bandwidth efficiency. Also, AF-TWR-D-STBC outperforms AF-TWR (when one relay is activated) even though the former contributes more interference. Finally, to further boost the performance, successive interference cancellation (SIC) is used to extract the spatial diversity offered by the relays.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".