Power Allocation and Scheduling for Broadband Wireless Networks Considering Mutual Interference
Why this work is in the frame
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Bibliographic record
Abstract
With the limited wireless spectrum and the ever-increasing demand for wireless services, how to enlarge wireless network throughput is a pressing issue. To exploit the wireless spatial capacity, concurrent transmissions, if controlled appropriately, can lead to overall higher spectrum utilization and network throughput. The optimal scheduling and power control for concurrent transmissions in rate-adaptive wireless networks is a very challenging NP-hard problem. In this paper, we propose an efficient power allocation and scheduling algorithm for concurrent transmissions which can improve network throughput with fairness consideration. We first formulate the optimal power allocation and scheduling problem, and convert the original non-convex problem into a series of convex problems using a two-phase approximation technique. Then, we propose the power and channel allocation with fairness (PCAF) algorithm to solve the problem efficiently. Extensive simulation results show the remarkable improvement in terms of both network throughput and fairness, comparing to the previous scheduling algorithms.
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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 it