Power Allocation and Decoding Order Selection for Secrecy Fairness in Downlink Cooperative NOMA With Untrusted Receivers Under Imperfect SIC
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Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique for enhanced spectral efficiency in the current and next-generation wireless networks. In this paper, we examine a realistic NOMA model where users, assisted by a regenerative relay, cannot be fully trusted. We address the challenge of ensuring secure access for these users while accounting for the error propagation in successive interference cancellation (SIC) during the decoding process. For such, we formulate and solve two optimization problems, viz. maximizing the minimum secrecy rate of the users and maximizing the sum secrecy rate of the users, while accounting for SIC errors and the constraint on the power budget. For each case, we derive the optimal power allocation solution to achieve positive secrecy rates despite imperfect SIC. Simulation results provide key insights on the obtained secrecy rates and power allocations, factoring in residual interference. The joint optimal solution for the decoding order and power allocation is compared with different benchmark schemes: optimal decoding order and equal power allocation, fixed decoding order and equal power allocation, fixed decoding order and optimal power allocation, and optimal decoding order and channel-based power allocation. Our proposed framework demonstrates average performance gains of about 47.62 dB, 50.79 dB, 54.02 dB and 39.83 dB over these schemes and, hence, the fact that the proposed framework can substantially improve the secrecy performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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