Resource management in interference channels with asynchronous users
Bibliographic record
Abstract
We consider a two-user interference channel where the users are not synchronous meaning there exists a delay between their transmitted codes. Assuming no user is aware of the location of the interference burst on its code, no interference cancellation is performed, i.e., users treat each other as noise. By the same token, the interference is no longer Gaussian as a result of the ambiguity on the start of the interference burst. We propose a stationary channel model for this setup for which we are able to derive the achievable rates based on upper and lower bounds on the mutual information between the input and output of the channel. These bounds meet each other as the code length grows to infinity. We define the outage capacity for each user as the largest transmission rate such that the outage probability is ensured to be below a certain threshold. In case the users are sharing a certain number of frequency sub-bands, we propose to divide the spectrum among the users to maximize the outage capacity for each user. We demonstrate that depending on the probabilistic parameters of the delay model and the value of the outage threshold, there are cases where the best strategy is to assign both private and common frequency sub-bands to the users.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".