Throughput-delay trade-off of CSMA policies in wireless networks
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Bibliographic record
Abstract
We consider CSMA policies for multihop wireless networks. CSMA policies are simple policies that can be easily implemented in a distributed manner. However, the delay performance of CSMA policies can be very poor as the delay can grow exponentially in the network size. As a result, CSMA policies are not practical for delay-sensitive traffic even for mid-sized networks. In this paper, we consider a slight variant of the classical CSMA policies and show that it leads to a much improved delay performance. In particular, we show that the delay does not depend on the networks size. Using this result, we also characterize the delay-throughput trade-off of the proposed CSMA policy. At the heart of our analysis is a result that shows that CSMA policies quickly converge to a maximum schedule, i.e., converge to a maximum schedule at a rate that does not depend on the network size. Using this insight, we consider a CSMA policy that periodically “unlocks” the transmission pattern of a CSMA policy, and show that this unlocking mechanism can be used to obtain a much improved delay performance without significantly reducing the throughput. While our analysis has been carried out for the special case of an interference graph with a grid (lattice) topology, we provide numerical case studies for general network topologies and show that the intuition obtained from the analysis carries over to these general cases. We also illustrate the performance of the proposed CSMA policy when combined with a flow control mechanism.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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