On the Performance of NOMA in the Two-User SWIPT System
Bibliographic record
Abstract
In this paper, we investigate nonorthogonal multiple access (NOMA) for a simultaneous wireless information and power transfer (SWIPT) system consisting of a battery powered access point and two energy harvesting users. In SWIPT, there exists an information-energy tradeoff. Since the adoption of NOMA may cause more energy consumption than the orthogonal multiple access (OMA), it is not known whether NOMA can always enhance the spectral efficiency in SWIPT systems compared to OMA. We prove that NOMA performs better than OMA when the decoding energy consumption is negligible. However, for the nonnegligible decoding energy consumption, we show that NOMA is not always superior to OMA. Interestingly enough, for the nonnegligible decoding energy consumption, OMA can outperform NOMA when the channel power gains of the two users are not sufficiently different. Moreover, we study the performance of cognitive radio inspired NOMA (CR-NOMA), in which the power is allocated to the user with poor channel condition such that its quality of service (QoS) requirement is met. Different from the battery powered CR-NOMA, the analytical results show that CR-NOMA can outperform OMA in SWIPT systems only if the channel power gains of users are sufficiently different and the QoS of the weak user is above a threshold.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".