Secure Communication in OFDMA-Based Cognitive Radio Networks: An Incentivized Secondary Network Coexistence Approach
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it