NOMA for Wireless-Powered Communication Networks With Buffered Sources
Bibliographic record
Abstract
To increase the feasibility of wireless powered communication networks (WPCNs), the synergy of efficient multiple access protocols and diverse types of resources, e.g., multiple antennas, memories, and energy storage devices is of paramount importance. To this end, in this paper, we introduce the use of buffered sources in WPCNs with uplink non-orthogonal multiple access, in which a multi-antenna hybrid access point can both transmit energy and receive information. The proposed scheme aims at maximizing the long-term utility function, which is determined by the achieved average data rate and fairness among sources. The formulated long-term time-average optimization problem is converted into instantaneous ones by exploiting the Lyapunov optimization framework. The derived analytical solutions reveal the impact of data buffers’ length on the decoding order of sources’ messages. Finally, it is shown that the proposed scheme achieves superior long-term utility compared to the considered baseline schemes with non-buffered sources and buffered sources with orthogonal multiple access. Additionally, it is demonstrated that our proposed scheme can ensure fairness without reducing the average data rate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".