A Distributed Pulse-Based Synchronization Protocol for Half-Duplex D2D Communications
Bibliographic record
Abstract
In distributed device-to-device (D2D) communications, no common reference time is available and the devices must employ distributed synchronization techniques. In this context, pulse-based synchronization, which can be implemented by distributed phase-locked loops is preferred due to its scalability. Several factors degrade the performance of pulse-based synchronization, such as duplexing scheme, clock skew and propagation delays. Furthermore, in distributed networks, devices should be aware of the synchronization status of others in order to initiate data communications. To address these prevailing issues, we first introduce a half-duplex timing-advance synchronization algorithm wherein each device alternates between being a transmitter and receiver in their exchange of synchronization pulses at each clock period. Based on this algorithm, we propose a novel fully-distributed pulse-based synchronization protocol for half-duplex D2D communications in 5G wireless networks. The protocol allows participating devices to become aware of the global synchronization status, so that they can complete the synchronization process ideally at the same time and proceed to data communication. In simulation experiments over multi-path frequency selective channels, the proposed synchronization protocol is shown to outperform a benchmark approach from the recent literature over a wide range of conditions, e.g., clock skew, number of devices, and network topology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".