Analysis of <inline-formula> <tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Tier Uplink Cellular Networks With Ambient RF Energy Harvesting
Bibliographic record
Abstract
We use stochastic geometry to develop a comprehensive modeling framework for K-tier uplink cellular networks with RF energy harvesting from the concurrent cellular transmissions. In the considered system model, channel inversion power control is used and cellular users are equipped with energy storage units. We also use tools from queueing theory, namely, Markov chain analysis, to model the level of stored energy in each user's battery. A successful transmission is assumed only when the amount of energy stored in a user's battery is sufficient to perform channel inversion with a received signal-to-interference ratio (SIR) above a predefined threshold. The performance of the proposed system model is evaluated in terms of the transmission probability, the (SIR) coverage probability, and the overall success probability. Using Poisson point processes (PPPs) enables us to derive simple expressions for these performance metrics in order to obtain insights for network design and optimization. We show the effect of varying the different network parameters such as the spatial density of BSs and the receiver sensitivity. In addition, we discuss several special cases and provide guidelines on the extensions of the proposed framework. We show that the gain of using RF energy harvesting can be highly improved by a proper choice of the network design parameters.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".