Capacity of Wireless Multi-hop Networks Using Physical Carrier Sense and Transmit Power Control
Bibliographic record
Abstract
In this paper, we investigate the capacity of CSMA (carrier sense multiple access) based wireless multi-hop networks with random topologies described by the hop length distribution. First we develop an analytical model for the effective link capacity as a function of transmit power adaptation policy, physical carrier sense threshold, hop length distribution and medium access probability of p-persistent CSMA. Secondly, we devise an optimal transmit power control scheme that maximizes the network capacity by adjusting the transmit power and the corresponding physical carrier sense threshold. Thereafter, it is extended for the joint optimization of these parameters with the medium access probability of CSMA. Finally we compare the optimal power control scheme with the minimum transmit power policy in. Results show that the proposed power control scheme optimally trades off the spatial reuse (number of interfering links) to the link SIR (signal-to-interference ratio) and achieves an amount of ~%15 increase in network capacity when both schemes employ joint optimization.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".