Multi-user scheduling schemes for simultaneous wireless information and power transfer
Bibliographic record
Abstract
In this paper, the downlink multi-user scheduling problem is studied for a time-slotted system with simultaneous wireless information and power transfer. In particular, in each time slot, a single user is scheduled to receive information, while the remaining users opportunistically harvest the ambient radio frequency (RF) energy. We devise novel scheduling schemes in which the tradeoff between the users' ergodic capacities and their average amount of harvested energy can be controlled. To this end, two fair scheduling schemes used in information-only transfer systems are modified. First, proportionally fair maximum normalized signal-to-noise ratio (N-SNR) scheduling is modified by scheduling the user having the jthascendingly ordered (rather than the maximum) N-SNR. We refer to this scheme as order-based N-SNR scheduling. Second, conventional equal-throughput (ET) fair scheduling is modified by scheduling the user having the minimum moving average throughput among the set of users whose N-SNR orders fall into a certain set of allowed orders Sa(rather than the set of all users). We refer to this scheme as order-based ET scheduling. The feasibility conditions required for the users to achieve ET with this scheme are also derived. It is shown that the smaller the selection order j for the order-based N-SNR scheme, and the lower the orders in Safor the order-based ET scheme, the higher the average amount of energy harvested by the users at the expense of a reduction in their ergodic capacities. The performance of the considered scheduling schemes is analyzed for independent and non-identically distributed (i.n.d.) Ricean fading channels, and closed-form results for the special case of i.n.d. Rayleigh fading are provided.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".