Power Allocation and Decoding Order Selection for Secrecy Fairness in Downlink Cooperative NOMA With Untrusted Receivers Under Imperfect SIC
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".