Performance Characterization of Spatially Random Energy Harvesting Underlay D2D Networks With Transmit Power Control
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Bibliographic record
Abstract
In underlay device-to-device (D2D) networks, the transmitter nodes can harvest energy from downlink cellular (primary) transmissions to solely power the D2D links, which enhances the overall spectral and energy efficiencies. How will the energy harvest and D2D link performance be affected by spatial randomness, temporal correlations, transmit power control, and channel uncertainties? To investigate these issues, we analyze the energy harvesting process of a random (typical) D2D transmitter node, say Dt, which needs a sufficient harvest to meet the requirements for receiver sensitivity and channel inversion. This system model consists of: 1) three independent homogeneous Poisson point processes; 2) log-distance path loss and Rayleigh fading; and 3) path loss inversion transmit power control. We derive the ambient radio frequency energy at Dt, and model the harvest as a Gamma random variable. We propose four schemes: 1) single slot harvesting; 2) multislot harvesting; 3) i1'( slot harvesting; and 4) hybrid harvesting. We develop a Markov chain model for success probability of these schemes, and derive the D2D coverage. We find that a high density of primary transmitters is unfavorable to multislot harvesting for increased D2D link distances. Moreover, hybrid harvesting always outperforms single and i1'( slot harvesting, and outperforms multislot harvesting except for very high path-loss conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it