Distributed Stable Multisource Global Broadcast for SINR-Based Wireless Multihop Networks
Bibliographic record
Abstract
Multi-source global broadcast is a fundamental problem in multi-hop wireless networks. The Static Multi-source Global Broadcast problem (SMGB), which considers static packet injection at all source nodes, has been extensively studied in recent years. However, packets are more likely to be continuously injected over time in realistic multi-hop wireless networks. In this paper, we focus on studying the Dynamic Multi-source Global Broadcast problem (DMGB), in which packets are continuously injected to$k$($k\geq 2$) source nodes in the network according to a widely-used dynamic packet injection model and the objective is to disseminate each injected packet across the whole network quickly. We solve this DMGB problem under the Signal-to-Interference-plus-Noise-Ratio (SINR) interference model. Specifically, we first present a distributed randomized algorithm for solving the SMGB problem. We then iterate this SMGB algorithm repeatedly to construct a distributed DMGB algorithm. We prove the proposed DMGB algorithm is stable, i.e., the expected number of packets in each node’s message queue is bounded at any time and further the expected global broadcast latency for each injected packet is bounded. Simulation results validate the effectiveness of the proposed DMGB algorithm.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".