Scheduling in wireless ad hoc networks with successive interference cancellation
Bibliographic record
Abstract
Successive interference cancellation (SIC) is an effective way of multipacket reception (MPR) to combat interference in wireless networks. To understand the potential MPR advantages, we study link scheduling in an ad hoc network with SIC at the physical layer. The fact that the links detected sequentially by SIC are correlated at the receiver poses key technical challenges. We characterize the link dependence and propose simultaneity graph (SG) to capture the effect of SIC. Then interference number is defined to measure the interference of a link. We show that scheduling over SG is NP-hard and the maximum interference number bounds the performance of maximal greedy schemes. An independent set based greedy scheme is explored to efficiently construct a maximal feasible schedule. Moreover, with careful selection of link ordering, we present a scheduling scheme that improves the bound. The performance is evaluated by both simulations and measurements in testbed. The throughput gain is on average 40% and up to 120% over IEEE 802.11. The complexity of SG is comparable with that of conflict graph, especially when the network size is not large.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".