Artificial Noise Aided Secure Communications for Cooperative NOMA Networks
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) has been envisioned as a promising multiple access technique to improve spectral efficiency and provide massive connectivity in future wireless networks. However, the inherited security issues with NOMA should be carefully addressed to further exploit its potential benefits in NOMA enabled wireless networks. As such, we consider a cooperative NOMA network, where a source uses the NOMA to simultaneously communicate with a multi-antenna near-user and a far-user. While directly communicating with the near-user, the source employs multiple full-duplex (FD) decode-and forward (DF) relays to establish communication with the far-user in the presence of a passive eavesdropper. To address the eavesdropping in this cooperative NOMA network, we propose a new two-phase FD-based artificial noise (AN) scheme with different relay selection techniques. In the first phase, the selected FD relay emits AN to confuse the eavesdropper while receiving the superimposed signal from the source. In the second phase, the selected relay performs exclusive OR (XOR) operation on both the message intended to the far-user and the AN before broadcasting the resulting mixed signal. By utilizing null-space beamforming, self-interference cancellation techniques and DF-XOR cooperative protocol, the AN in the proposed scheme can be efficiently eliminated at the near-user and far-user as well as at the selected relay. However, the AN cannot be suppressed at the eavesdropper which serves the purpose of AN through degrading the decoding capability of the eavesdropper. We evaluate the performance of the proposed scheme in terms of security-reliability trade-off (SRT). For the AN-aided scheme with max-min and partial relay selection techniques, we theoretically derive the exact and asymptotic closed-form expressions of the outage probability and intercept probability. Numerical results have been provided to validate the derivations. In addition, the results reveal that the SRT of the near-user and far-user can be improved by increasing the number of antennas at the near-user and the number of relays.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".