An online resource allocation algorithm to minimize system interference for inband underlay D2D communications
Bibliographic record
Abstract
Summary This paper addresses the research question of total system interference minimization while maintaining a target system sum rate gain in an inband underlay device‐to‐device (D2D) communication. To the best of our knowledge, most of the state of the art research works exploit offline resource allocation algorithms to address the research problem. However, in Long‐Term Evolution (LTE) and beyond systems (4G, 5G, or 5G+), offline resource allocation algorithms do not comply with the fast scheduling requirements because of the high data rate demand. In this paper, we propose a bi‐phase online resource allocation algorithm to minimize the total system interference for inband underlay D2D communication. Our proposed algorithm assumes D2D pairs as a set of variable elements whereas takes the cellular user equipment (UEs) as a set of constant elements. The novelty of our proposed online resource allocation algorithm is that it incurs a minimum number of changes in radio resource assignment between two successive allocations among the cellular UEs and the D2D pairs. Graphical representation of the simulation results suggests that our proposed algorithm outperforms the existing offline algorithm considering number of changes in successive allocation for a certain percentage of sum rate gain maintaining the total system interference and total system sum rate very similar.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".