Randomized on-off signaling for asynchronous interference channels
Bibliographic record
Abstract
This paper addresses a Gaussian interference channel consisting of two active users. The channel from each transmitter to each receiver is modeled with quasi-static and non-frequency selective Rayleigh fading. Users are asynchronous, meaning there exists a mutual delay between their transmitted codes. Due to the randomness of delay, no user is aware of the location of the interference burst along its code. As such, no interference cancellation is performed, i.e., users treat each other as noise. By the same token, interference has a mixed Probability Density Function (PDF) as a result of ambiguity on the start of the interference bursts. A stationary model for interference is considered by assuming the starting point of an interferer's codeword is uniformly distributed along the codeword of any user. Due to non-ergodicity of the model, outage analysis is an appropriate tool to study the network. All users follow a locally Randomized Masking (RM) signaling scheme where each transmitter quits transmitting its Gaussian signals independently from transmission to transmission with a certain probability. An upper bound on the probability of outage per user is developed using entropy power inequality and a key upper bound on the differential entropy of a mixed Gaussian random variable. It is shown that by adopting the RM scheme, the probability of outage is strictly lower than that of a scenario where both users keep transmitting their Gaussian signals known as continuous transmission.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".