Performance Analysis of Overlay Cognitive NOMA Systems With Imperfect Successive Interference Cancellation
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) and cognitive radio (CR) are envisioned as promising solutions to achieve high spectral efficiency for future wireless networks. This work investigates the outage performance of an overlay cognitive NOMA system with imperfect successive interference cancellation (SIC). The outage probability of primary user and secondary user are derived in closed forms. To obtain further insights, asymptotic expressions of outage probability and system throughput are evaluated when the transmit signal-to-noise ratio approaches infinity. An optimal power allocation coefficient is provided to maximize the system throughput. Moreover, the overlay cognitive NOMA system is compared with the underlay CR system and the overlay cognitive orthogonal multiple access (OMA) system in terms of outage probability. Finally, the performance analysis is validated by simulations. The simulation results demonstrate that the outage performance and system throughput of overlay cognitive NOMA system are superior to those of overlay cognitive OMA system and underlay CR system when the imperfect SIC satisfies certain conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| 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".