Bibliographic record
Abstract
A decentralized network of one Primary User (PU) and several Secondary Users (SU) is studied. The number of SUs is modeled by a random variable with a globally known distribution. PU is licensed to exploit the resources, while the party of SUs intend to share the resources with PU. Each SU must guarantee to not disturb the performance of PU beyond a certain level, while maintaining a satisfactory quality of service for itself. It is proposed that each secondary transmitter adopts a Randomized Masking (RM) strategy where it remains silent or transmits a symbol in its codeword independently from transmission slot to transmission slot. We consider a setup where the primary transmitter is unaware of the channel gains, the code-book of the secondary users and the number of secondary users. Although the SUs are anonymous to each other, i.e, they are unaware of each other's code-book, however, each SU is smart in the sense that it is aware of the code-book of PU, the channel gains and the number of active SUs. Invoking the concept of ε-outage capacity, we define the ε-admissible region as the set of possible transmission rates for PU and possible masking probabilities for each SU such that the probability of outage for PU is maintained under a threshold ε. Thereafter, the transmission rate of PU and the masking probability of SUs are designed through maximizing a globally known utility function of the rates of users over the ε-admissible region. In our analysis, the primary receiver treats interference as noise, however, each secondary receiver has the option to decode interference caused by PU, while treating the signals of other SUs as noise. In another approach, referred to as Power Control (PC), each SU transmits continuously (no masking is applied), however, it regulates its transmission power in order to yield the largest value for the utility function. It is demonstrated that PC offers a better performance in a regime where the transmission power for PU is relatively low, while RM outperforms PC if the transmission power for PU is sufficiently large.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".