Towards a Win-Win Spectrum Sharing Channel: A Secrecy Perspective
Bibliographic record
Abstract
Spectrum sharing and device-to-device (D2D) transmission are among the key features of modern communication networks. In this work, we are particularly interested in these two techniques from a sharing for secrecy perspective. The considered communication model consists of a multi-user cellular system and an underlying secondary system comprising K D2D pairs. All cellular and D2D transmissions are subject to an eavesdropping attack. Given a predefined secrecy condition, imposed by the primary system to guarantee a desired secrecy throughput, KS D2D pairs are allowed to share the spectrum and send their secret data while the remaining K-KS device transmitters operate as cooperative jammers. First, we characterize the achievable secrecy rates for both systems under a joint secrecy constraint on all transmitted cellular and D2D messages. Then, we propose a device selection scheme to determine the optimal number of devices, KS, that maximizes the secrecy throughput of the secondary system while satisfying the primary's secrecy condition. The obtained results show that both parties can win under the proposed transmission scheme; the cellular system can significantly improve its secrecy throughput, and the D2D pairs get to share the spectrum and achieve secure transmissions.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".