Performance Analysis of CR-Enabled AmBC NOMA Under IQI and Sensitivity Constraints
Bibliographic record
Abstract
Considering tag sensitivity and in-phase and quadrature-phase imbalance (IQI), this paper investigates the reliability and security of a cognitive radio (CR)-aided ambient backscatter communication (AmBC) non-orthogonal multiple access (NOMA) system. Herein, the source communicates with its NOMA users in the presence of an eavesdropper, and to bolster security, artificial noise (AN) injection is introduced. The effects of non-ideal successive interference cancellation (SIC) and AN removal on system performance are also examined. Subsequently, outage probabilities (OPs) and interception probabilities (IPs) of the tag and NOMA signals are derived, with asymptotic results for further insights. Finally, simulation results are provided to validate the theoretical analysis. It is observed that both reliability and security remain stable even as interference or maximum transmission power increases up to a certain threshold. Furthermore, the reliability under NOMA mechanism outperforms that of orthogonal multiple access. Notably, IQI can consistently enhance the confidentiality of NOMA but selectively strengthen backscatter transmission, albeit at the expense of reliability. This paper comprehensively underscores the potential for real-world implementation by incorporating sensitivity constraints, IQI, AN, and imperfect SIC into the system model.
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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 | 0.001 |
| 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".