Energy efficiency of cooperative D2D communications underlaying LTE-A networks
Bibliographic record
Abstract
Device-to-device (D2D) communications underlaying LTE-A networks is expected to bring significant benefits for resource utilization and energy efficiency (EE) improvement of user equipment (UE). However, the allocation of radio and power resources to D2D communications needs elaborate coordination, because of the interference between D2D communications and cellular communications. In this paper, we propose an energy-efficient cooperative D2D communication (EECD2D) technique using a power allocation algorithm, aiming at maximizing EE introduced by D2D communications in LET-A networks. Specifically, we define four D2D and cellular combinations based on distances, and analyze average EE of EECD2D and that of cooperative D2D communications without optimization. Results show that average EE of our algorithm is much higher than that without optimization, and closer D2D cooperators and distant cellular UEs whose uplink resource is reused, achieve highest average energy efficiency.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".