A Distributed Prioritized Multiple Access Scheme for Ad Hoc Networks Using Time-Frequency Hopping Communications
Bibliographic record
Abstract
In the time-frequency hopping (TFH) communications, a terminal transmits data at pseudo-random time slots and carrier frequencies, leading to high security against eavesdropping and anti-interference capability. However, in an ad hoc network using the TFH communications, it is challenging to coordinate stations in a distributed manner to share the time/frequency resources, avoid collision, and provide service differentiation. In this paper, we propose a distributed, prioritized multiple access control (MAC) protocol for TFH-based ad hoc networks, named TFH-MAC. In this scheme, the channel occupancy ratio (COR) is introduced to indicate the transmission of stations in a matrix of time-frequency resource blocks. By assigning different predetermined COR thresholds and contention window sizes to traffic classes, the priorities in channel access can be provided. Furthermore, the low-complexity random linear coding (RLC) is employed to repair frames from time-frequency spread segments with partial collision. In particular, the segments correctly received in previous transmissions can be combined with newly successfully received segments for decoding. Thus, the transmission efficiency is increased. An analytical model using mean value analysis is proposed to study the performance of saturated TFH-MAC theoretically, and the transmission probability, collision probability, throughout, and frame service time are derived. Extensive simulation results have verified the analytical model and demonstrated the optimal traffic load and resource matrix dimension to maximize the network throughput.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".