Secrecy Performance of Friendly Jammer Assisted Cooperative NOMA Systems with Internal Eavesdroppers
Bibliographic record
Abstract
In non-orthogonal multiple access (NOMA) systems, serving multiple users in shared resource blocks can allow untrusted users to overhear the messages of other users. In this context, we study a network consisting of a base station (BS), a near user and a far user, where the latter attempts to overhear the message of the former. The near user is a full-duplex (FD) node that can also act as a relay. Two operating scenarios are considered: 1) friendly jammer (FJ), where the FD node broadcasts noise for degrading the channel between the BS and the far user, while receiving data from the BS; and 2) friendly jammer relay (FJR), where, in addition to degrading the channel between the BS and the far user, the FD node relays the message of the far user. We investigate the secrecy performance of the network by characterizing the secrecy outage probability (SOP) in both scenarios. We obtain the exact SOP of the FJ case, and an approximation for the SOP of the FJR scenario, both expressed in closed-form. Numerical results confirm the accuracy of the analytical results. For a given BS power budget and power allocation to the users, it is demonstrated that the jamming and relaying powers are prominent factors to make the NOMA-FJR scheme superior to NOMA-FJ, as well as to conventional and cooperative NOMA schemes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".