Power allocation for a hybrid energy harvesting relay system with imperfect channel and energy state information
Bibliographic record
Abstract
In this paper, we consider both channel state uncertainty and harvested energy state uncertainty for a source-relay-destination communication link where the source and the relay are equipped with hybrid energy sources. Taking into account these uncertainties is of important for practical energy harvesting (EH) communication. While channel state uncertainties also affect conventional communication systems and have been widely studied, harvested energy state uncertainties are specific to energy harvesting systems and have not been considered in the literature before. The considered hybrid energy sources include a constant energy source and an energy harvester. Our objective is to maximize the worst case system throughput over a finite number of transmission intervals. We propose robust optimal offline, optimal online, and suboptimal online power allocation schemes. The offline power allocation design is formulated as an optimization problem which can be solved optimally. For the online case, we propose a dynamic programming (DP) approach to compute the optimal transmit power. To alleviate the prohibitively high complexity inherent to DP, we also propose several suboptimal low-complexity online power allocation schemes. Simulation results confirm the robustness of the proposed power allocation schemes to channel and energy state uncertainties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".