Joint Prioritized Scheduling and Resource Allocation for OFDMA-Based Wireless Networks
Bibliographic record
Abstract
In this paper, we study the joint prioritized link scheduling and resource allocation for OFDMA-based wireless networks, which serve two classes of wireless links, namely, non-prioritized (low-priority) and prioritized (high-priority) links. Our design aims to maximize the number of scheduled non-prioritized links and their sum rate, while guaranteeing the minimum required rates of all active prioritized and non-prioritized links. We present the problem formulation as a single-stage optimization problem, which simultaneously maximizes the number of scheduled non-prioritized links and their sum rate. We propose a monotonic-based optimal approaching (MBOA) algorithm to solve this problem by employing the monotonic global optimization technique and an efficient rounding procedure. We prove that the MBOA algorithm can schedule the maximum number of non-prioritized links with slight and controllable degradation in the minimum required rates of non-prioritized links. For low-complexity design, we propose an iterative convex approximation algorithm, which sequentially performs power allocation and link removal in each iteration. We then describe how the proposed algorithms can be implemented in the standardized LTE-based cellular system. Finally, we conduct numerical studies for device-to-device communications underlaid cellular networks under perfect or imperfect channel state information (CSI). Numerical results demonstrate that the proposed algorithms can be applied to the imperfect CSI scenario with slight degradation in the network performance. Moreover, in the perfect CSI scenario, the proposed algorithms significantly outperform the conventional algorithms both in the number of scheduled non-prioritized links and their sum rate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".