Novel Mathematical Framework for Performance Analysis of Energy Harvesting-Based Point-to-Point Communications
Bibliographic record
Abstract
This paper presents a novel performance evaluation framework for energy harvesting communications. As the harvested energy may not always be at the required levels in the transmitter’s battery, possible energy outage may hinder the transmission, especially in weak channel conditions. Herein, we analyze the performance of an energy harvesting communication link by allowing a certain level of energy outage to occur. Such operation is challenging, given that the energy coming into the battery from an uncontrollable source, e.g., solar energy, does not relate to the channel conditions and quality-of-service (QoS) requirement, whereas energy going out of the battery is directly dependent on both. Hence, the incoming energy and outgoing energy become independent of each other. Knowing the exact level of energy that is accumulated in the battery is therefore challenging. To deal with these challenges, a probabilistic energy-outage approach and a virtual battery queuing model are proposed and used to develop the target performance evaluation framework while leveraging the large deviation principle theorem. The derived energy-outage probability of the communication system relates the system parameters, namely, QoS component, channel conditions, and harvested energy. Numerical results are presented to confirm the analytical findings and discuss the performance of energy harvesting based communication with tolerable energy-outage as a function of the system 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".