Delay-Throughput Analysis in Decentralized Single-Hop Wireless Networks
Bibliographic record
Abstract
In this paper, an asymptotic analysis for the delay-throughput of a single-hop wireless network with n pairs of nodes is presented. The analysis relies on the decentralized on-off power allocation strategy, in which the on-off transmission policy for each link is based on comparing its direct channel gain with optimum threshold τn. We first provide a new definition of the transmission delay in a homogenous network. It is proved that the delay threshold level that results the dropping probability for each link tends to zero, while achieving the maximum average sum-rate scales as ω(n / log n). Also, the minimum delay in order to make the dropping probability for the whole network approach zero scales as ω(n / log n) + n. Furthermore, we drive lower and upper bounds for the link activation probability, q, such that the order of the average sum-rate is preserved. Based on the upper bound on q, an asymptotic analysis shows that the delay in each link and in the network improves without any significant impact on the the average sum-rate. Finally, we present a new definition of the throughput for the link in the cases of one and infinite buffer size. It is demonstrated that the maximum average throughput of the network with the decentralized on-off power allocation strategy is independent of the buffer size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".