Resource Allocation for Energy Harvesting Assisted D2D Communications Underlaying OFDMA Cellular Networks
Bibliographic record
Abstract
Device-to-Device (D2D) communications underlaying cellular communications has been explored in the literature for a while since the benefits of enhanced sum throughput and more efficient spectrum usage have been proven very promising through the activation of direct transmissions between a pair of devices. To achieve better performance in terms of energy preservation, we consider introducing energy harvesting (EH) mechanism into the traditional D2D model. Our aim is to maximize sum throughput for D2D users without compromising the QoS performance of cellular users (CUs) in an EH-aided communications model. D2D transmissions will only be activated at the beginning of a time slot if there remains enough energy, which is set as a lower threshold, for one-slot data transmission in the batteries of D2D users. Otherwise, it will switch into energy harvesting mode until the energy level in the batteries rises back to an upper threshold. The formulated optimization problem is a nonlinear mixed integer problem. Since it is mathematically challenging to get an optimal solution, we aim for a suboptimal solution with an iterative joint resource block and power resource allocation algorithm. Then we compare this heuristic algorithm with a simplified version where the constraints to make sure that every D2D user has at least one RB for communications are slighted. Numerical simulation results show that energy harvesting mechanism can efficiently power D2D communications underlaying cellular networks. They also corroborate higher sum throughput under different parameter settings of our first proposed approach.
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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 | 0.001 | 0.000 |
| 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".