Optimal Allocation of Scarce Pilot Resources for OFDMA-Based Underwater Acoustic Sensor Networks
Bibliographic record
Abstract
Underwater acoustic sensor networks (UASNs) constitute the primary infrastructure for the Internet of Underwater Things (IoUT), but they face constraints in high-rate reliable transmission due to the complex underwater acoustic (UWA) channel characteristics and the scarce pilot resources. Orthogonal frequency division multiple access (OFDMA) has been widely adopted in UASNs owing to its capability for multi-user parallel transmission and flexible resource allocation. However, the severely limited and non-uniformly distributed pilots pose significant challenges for multi-user channel estimation in OFDMA-based UASNs. To address these challenges, this paper establishes a compressed sensing (CS)-based multi-user channel estimation model for OFDMA-based UASNs. Considering the significant variations in channel conditions among different UWA links, we propose an adaptive pilot allocation method that dynamically optimizes the number of pilots assigned to each user. Building on this, we further propose a bi-objective joint pilot position and power optimization (BOJPO) algorithm, which simultaneously minimizes both the mutual coherence and total coherence of the measurement matrix to enhance estimation accuracy. Simulation and sea experiment results demonstrate that the proposed pilot optimization scheme exhibits superior performance, delivering stable and reliable channel estimation for OFDMA-based UASNs despite severely scarce pilot resources.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".