Simultaneous wireless information and power transfer in interference alignment networks
Bibliographic record
Abstract
Interference alignment (IA) is a promising solution for interference management in wireless networks. On the other hand, simultaneous wireless information and power transfer (SWIPT) has become an emerging technique. Although some works have been done on IA and SWIPT, these two important areas have traditionally been addressed separately in the literature. In this paper, we propose to use a common framework to jointly study IA and SWIPT. We analyze the performance of SWIPT in IA networks. Specifically, we derive the upper bound of the power that can be harvested in IA networks. In addition, we show that, to improve the performance of wireless power transfer and information transmission, users should be dynamically selected as energy harvesting or information decoding terminals. Furthermore, we design two SWIPT-user selection (SWIPT-US) algorithms in IA networks. Simulation results are presented to show the effectiveness of the proposed algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".