Distributed Stable Global Broadcasting for SINR-Based Multi-Channel Wireless Multi-Hop Networks
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Bibliographic record
Abstract
Recently, the problem of Stable Global Broadcasting (SGB) with continuous packet injections at a source node has attracted considerable attention and much work has been carried out. However, existing SGB algorithms are all centralized and under the graph-based interference model. How to achieve efficient distributed SGB under the more realistic Signal-to-Interference-plus-Noise-Ratio (SINR) interference model is still an open issue. In this paper, we focus on the design of distributed SGB algorithms for multi-channel wireless multi-hop networks under the SINR model. We first present an efficient Backbone-based Multi-channel Concurrent Scheduling (BMCS) strategy and prove an upper bound of $1/2$ packets/slot for the broadcast capacity (i.e., the maximum supportable packet injection rate for all possible SGB algorithms). By iterating the BMCS strategy in different ways, we present two distributed SGB algorithms, one for deterministic packet injection model corresponding to the broadcast capacity (called SGB-DPI) and another for stochastic packet injection model (called SGB-SPI). We prove that: 1) both SGB-DPI and SGB-SPI meet the queue stability and latency stability constraints for providing stable global broadcasting services, and 2) SGB-DPI is throughput-optimal. We evaluate the performance of our proposed algorithms through simulations. The simulation results validate our theoretical analysis and also show that even under the stochastic packet injection model, SGB-DPI has a comparable and even better throughput performance than SGB-SPI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it