Prioritised and selective power control in cellular wireless networks
Bibliographic record
Abstract
Power control is used in cellular communications systems to reduce power consumption and satisfy as many users as possible by managing the mutual interference between users. In signal to interference plus noise ratio (SINR) tracking power control (TPC) schemes, all users are required to adjust their power levels for each iteration, which is inefficient. In this study, a prioritised and selective uplink power control scheme is proposed. Priority user requirements are satisfied first, and then as many normal users (NUs) as possible are satisfied with their target SINRs. In addition, if an NU is currently satisfied with its target SINR, it is not required to update its transmit power level. Conversely, NUs who are not satisfied update their power levels. Simulation results are presented, which show that the proposed scheme outperforms dynamic target SINR TPC and variable target SINR TPC in terms of power consumption and efficiency. In addition, the proposed scheme is better than target SINR TPC and opportunistic power control as the number of users increases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".