Interference and throughput aware resource allocation for multi‐class D2D in 5G networks
Bibliographic record
Abstract
This study examines subcarrier and optimal power allocation in orthogonal frequency division multiple access based 5G device‐to‐device (D2D) networks. To improve spectrum efficiency, D2D users share same subcarriers with the legacy users using underlay approach. In this approach, it is challenging to design an efficient subcarrier and power allocation method for D2D networks which guarantees the quality of service requirements of legacy users. Therefore, the key constraint is to check the interference condition among D2D and legacy users while allocating the same resources to D2D users. In this study, the authors propose a throughput efficient subcarrier allocation (TESA) and geometric water‐filling based optimal power allocation (GWFOPA) method for multi‐class cellular D2D systems. First, the TESA method selects subcarriers and allocates power equally for D2D users according to their service classes while maintaining interference and data rate constraints. Then, the GWFOPA method is applied to optimise power in a computationally effective way. The objective of TESA and GWFOPA method is to maximise the data rate of each class while maintaining interference constraint and fairness among the D2D users. Finally, the authors present simulation results to evaluate performance of TESA and GWFOPA in terms of throughput, user data rate, and fairness.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".