Performance analyses of TAS/Alamouti‐MRC NOMA in dual‐hop full‐duplex AF relaying networks
Bibliographic record
Abstract
Abstract In this article, performance of a multi‐user downlink non‐orthogonal multiple access system in dual‐hop full‐duplex amplify‐and‐forward relaying networks is investigated over Nakagami‐ fading channels in the presence of channel estimation error and feedback delay. The base station applies transmit antenna selection (TAS)/Alamouti space‐time block coding scheme, while the users exploit maximum‐ratio combining to utilize benefits of receive diversity. To demonstrate superiority of the proposed system, outage probability (OP) is investigated, and tight lower bound expression is derived to support the exact OP. Moreover, asymptotic analyzes are also conducted for ideal and practical conditions to provide further insights into the OP in high signal‐to‐noise ratio region. In addition, software defined radio based test‐bed implementation is realized to confirm the analysis and show feasibility of the proposed system. Theoretical analyzes verified by simulations and practical implementations demonstrate that more performance improvement can be achieved for the weakest user (with the lowest channel gain) in the case of practical conditions when compared to ideal cases. Also, both diversity order and array gain are dominant in achieving minimum OP (based on relay location) in ideal conditions, while only array gain has effect in practical manner.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".