PHY-aware distributed scheduling for ad hoc communications with physical interference model
Bibliographic record
Abstract
We consider a random-access-based ad hoc network, where different links use mini-slots to contend for the channel, and then successful links transmit data packets, as in CSMA. The focus of our study is to develop optimal strategies for physicallayer- aware (PHY-aware) distributed scheduling, which involves a joint process of channel probing and distributed scheduling. Because of channel fading and cochannel interference, the signalto- interference-plus-noise-ratio (SINR) across links is highly dynamic and can exhibit significant variation. In the low SINR case, further channel probing is likely to lead to better SINR conditions and hence yield higher throughput. The desired tradeoff boils down to judiciously choosing the optimal stopping strategy for channel probing before data transmissions. In this paper, we investigate PHY-aware distributed scheduling, aiming to maximize the overall network throughput. The problem under consideration is inherently challenging: 1) multiple links can transmit successfully simultaneously and the number of simultaneously transmitting links is random; and 2) the network throughput is the sum rate of all transmitting links, but each link involved in the transmission has no knowledge of the instantaneous rates of other links, and the stopping decision is made in a distributed manner based on local information only. We use optimal stopping theory to tackle this challenge, and show that the optimal policy for distributed scheduling has a threshold structure. Accordingly, after a channel probing, a link would proceed with data transmissions only if a function of its instantaneous rate is greater than the optimal rate threshold. Observing that the network throughput depends heavily on the contention probability of each link, we generalize the study to jointly optimize the rate threshold and the contention probability, and propose a two-stage algorithm for computing the pair of optimal rate threshold and contention probability by using fractional optimization and geometric programming.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".