Decoding Order and Power Control for Securing Priority Users in Cooperative NOMA-Enabled Industrial IoT Networks
Bibliographic record
Abstract
The advancement of industrial Internet of Things (IIoT) networks has brought challenges in terms of connectivity, efficient spectrum usage, and low latency. To tackle these challenges, advanced multiple access techniques have been developed. Non-orthogonal multiple access (NOMA) is a promising multiple access technique due to its high energy efficiency and fairness for devices. However, NOMA has inherent security issues due to wireless transmission and complex successive interference cancellation (SIC) based decoding, which can negatively impact system performance. Furthermore, achieving perfect SIC is also a challenging task due to implementation complexity. This study investigates the effects of imperfect SIC in a dual-device cooperative NOMA system. Our system includes direct links between the source and devices and uses both decode-and-forward and amplify-and-forward relays. The overall objective is to optimize decoding order and power allocation coefficients in order to maximize the near or priority device’s secrecy rate while meeting the quality-of-service (QoS) requirements of the far or normal device. Observing the underlying optimization problems to be non-convex, a low-complexity algorithm yielding optimal solutions is developed. Our findings reveal how imperfect SIC can impact massive access systems and secrecy performance. Extensive simulations provide novel design insights into the achievable secrecy rate and optimal power allocation coefficients. We also explore the trade-off between the priority device’s secrecy rate and the normal device’s rate, along with the impact of residual interference. Finally, our proposed solution has been shown to significantly improve the QoS-constrained secrecy rate of priority devices when compared to relevant benchmarks.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".