Spatial Reuse Time-Division Multiple Access for Broadcast Ad Hoc Underwater Acoustic Communication Networks
Bibliographic record
Abstract
Underwater acoustic communication (UWAC) is often the only viable solution to establish an ad hoc underwater communication network. The specific features of UWAC, arising from the physics of underwater acoustics, make the design of resource-efficient media access control (MAC) protocols important as well as challenging. In this paper, we tackle this task considering ad hoc UWAC networks that support high-traffic broadcast communication. To this end, we propose the application of the spatial reuse concept and the exploitation of direct sequence spread spectrum used at the UWAC physical layer to obtain a new hybrid spatial reuse time-division multiple-access (HSR-TDMA) protocol. By tracking the time-varying network topology, our protocol adaptively optimizes the set of active communication nodes and overcomes problems of UWAC networks such as the near-far problem, flickering, and formation of islands. Pertinent performance parameters, namely network availability, message reliability, and transmission rate, are analyzed for the proposed protocol. Evaluation of these analytical performance expressions demonstrates the significant advantages of HSR-TDMA over commonly used conventional TDMA for broadcast UWAC networks. We also report performance results for both the HSR-TDMA and the conventional TDMA protocol from a sea trial at the Haifa harbor, which corroborate the results obtained from the analysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".