Optimal Selective Transmission Policy for Energy-Harvesting Wireless Sensors via Monotone Neural Networks
Bibliographic record
Abstract
We investigate the optimal transmission policy for an energy-harvesting wireless sensor node. The node must decide whether an arrived packet should be transmitted or dropped, based on the packet's priority, wireless channel gain, and the energy status of the node. The problem is formulated under the Markov decision process (MDP) framework. For such a problem, the conventional method to get the optimal policy is by using a state value function, which is three-dimensional in the considered problem, leading to high complexity. Fortunately, to reduce complexity, we derive an equivalent solution for the optimal policy via a one-dimensional after-state value function. We show that the after-state value function is differentiable and nondecreasing. We also discover a threshold structure of the optimal policy that is derived by the after-state value function. Furthermore, to approximate the after-state value function, we propose a learning algorithm to train a three-layer monotone neural network. The trained network thus finds a near-optimal selective transmission policy of the node. Finally, through simulation, we demonstrate the learning efficiency of the algorithm and the performance of the learned policy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".