Secure Communication in OFDMA-Based Cognitive Radio Networks: An Incentivized Secondary Network Coexistence Approach
Bibliographic record
Abstract
In this paper, we propose a secure cooperative communications scheme for orthogonal frequency-division multiple-access (OFDMA) cognitive radio networks (CRNs), where a primary base station (PBS) wants to transmit information to some distant primary users (PUs) in the presence of a set of passive eavesdroppers. In our model, the transmission is performed in two consecutive time slots; in the first time slot, the PBS transmits while the secondary users (SUs) and the eavesdroppers listen. In the second time slot, the SUs transmit while the PUs, the secondary base station (SBS), and the eavesdroppers listen. We consider two schemes for eavesdropping; in the first scheme, the eavesdroppers listen to transmissions from the PBS to the SUs, and in the second scheme, we assume that the eavesdroppers apply the maximal ratio combining approach on the received signals in the first and second time slots for the primary network. In the proposed model, the SUs are allowed to use the licensed spectrum of the PUs, as long as they help the PUs to satisfy their secrecy rate requirement. We assume a frame-based transmission where each frame is divided into two consecutive time slots of equal duration. In the first time slot, the PBS transmits while the SUs and the eavesdroppers listen. In the second time slot, the selected SUs relay the PBS information to the distant PUs. Meanwhile, the SUs use the remaining resources to transmit their own information to the SBS while the eavesdroppers listen to this transmission. We formulate our proposed schemes as an optimization problem and solve it by dual Lagrange approach. We evaluate our proposed scheme in various situations using simulations and show the efficiency of the proposed scheme. An important aspect of the proposed paradigm is that replacing the conventional average interference threshold constraint by the primary secrecy rate constraint does not only decrease the secondary average secrecy rate with respect to the conventional case but can actually provide significantly higher secondary average secrecy rate as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".