Resource allocation in RF energy harvesting‐assisted underlay D2D communication
Bibliographic record
Abstract
Abstract Device to device (D2D) communication is capable to address the increasing demand for data rates in fifth generation (5G) and beyond networks. However, D2D communication is usually convoluted with interference scenarios since both D2D users and cellular users share the same spectrum resources. Furthermore, D2D systems can trace back to limited battery life. The battery life problem is becoming more challenging with the exponential increase of devices in the future networks. Therefore, efficient resource allocation schemes need investigation to offer better quality of service for both cellular and D2D users under the constraints of interference and energy. In this paper, we address these two problems (interference and energy) simultaneously by efficiently allocating resources in energy harvesting‐assisted underlay D2D communication. We propose a deterministic model in which D2D users harvest energy only when required. We propose a resource allocation scheme, which jointly allocate resources and transmit power. We formulate an optimization problem with an objective to maximize sum throughput of D2D system while satisfying constraints on quality of service, power, and interference. To solve the problem, we adopt the nonlinear optimization by mesh adaptive direct search algorithm to obtain the suboptimal solution. We show the effectiveness of the proposed scheme in comparison with existing algorithms through simulation results.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".