Age of Information Analysis for Full Duplex Cooperative SWIPT System: NOMA versus RSMA
Bibliographic record
Abstract
The Age of Information (AoI) is a critical metric in next-generation communication networks, quantifying data freshness essential for latency-sensitive applications in 6G systems, such as autonomous driving and industrial IoT. This paper presents an AoI analysis within a downlink full-duplex (FD) cooperative simultaneous wireless information and power transfer (SWIPT) system, employing rate-splitting multiple access (RSMA) for short packet communication to enhance timely data updates. By integrating RSMA with SWIPT and FD capabilities, we propose a robust framework to reduce the AoI. In this regard, closed-form expressions of the average block error rate of the RSMA-enabled FD cooperative SWIPT system are derived and validated via Monte Carlo simulations. The results demonstrate that RSMA outperforms non-orthogonal multiple access (NOMA) and FD cooperative SWIPT NOMA in terms of error performance, while also reducing the inherent system design complexity. Our findings reveal that RSMA is a promising approach for minimizing AoI across various system configurations, offering valuable insights for designing future 6G networks that prioritize low latency, high reliability, and data freshness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".