Adaptive Rate and Energy Harvesting Interval Control Based on Reinforcement Learning for SWIPT
Bibliographic record
Abstract
In this letter, we propose a new adaptive rate and energy harvesting interval control scheme to maximize the throughout subject to the average energy constraint in the multiple-input single-output simultaneous wireless information and power transfer system. We consider the realistic scenario of time-varying fading channel. In order to maximize the throughput and simultaneously to maintain the average energy required at the receiver, we first formulate a problem of jointly optimizing the rate and energy harvesting interval based on a Markov decision process (MDP) by using a regularization parameter. However, this MDP problem is difficult to directly solve because the channel transition probabilities (i.e., the model or the environment) are challenging to estimate in the practical systems. Thus, we propose an adaptive rate and energy harvesting interval control algorithm based on the model-free reinforcement learning technique. Numerical results demonstrate that the proposed scheme significantly outperforms the conventional scheme.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".