Spectrum‐sharing broadcast channels using fountain codes: energy, delay and throughput
Bibliographic record
Abstract
In this study, the authors develop a transmission protocol for cognitive radio networks, whereby fountain codes are exploited in the broadcast channels and secondary users help with the broadcast from the base station (BS) to primary users (PUs). With fountain codes, the BS broadcasts to the secondary transmitter (ST) as well as PUs simultaneously, and stops broadcasting once the ST has received sufficient codeword to decode the original information reliably. Then, the ST will resume the broadcasting to PUs until all of them can decode the original information successfully. While broadcasting, the ST transmits information over its own link, that is, to the secondary receiver, based on dirty paper coding technique. As such, the energy expenditure at the BS is reduced and, moreover, secondary links have more opportunities to access the licensed spectrum band. To evaluate the performance of the developed scheme, they analyse its energy expenditure, broadcast time as well as the throughput over secondary links, and achieve the corresponding closed‐form expressions. Compared with the traditional broadcast protocol without the secondary's help, illustrative numerical results substantiate the validity of the author's derivations, which also demonstrate the efficiency of the developed scheme both on the energy savings and on the spectrum utilisation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".