Sensing, Probing, and Transmitting Policy for Energy Harvesting Cognitive Radio With Two-Stage After-State Reinforcement Learning
Bibliographic record
Abstract
This paper considers joint optimization of spectrum sensing, channel probing, and transmission power control for a single-channel secondary transmitter that operates with harvested energy from ambient sources. At each time slot, to maximize the expected secondary throughput, the transmitter needs to decide whether or not to perform the operations of spectrum sensing, channel probing, and transmission, according to energy status and channel fading status. First, we model this stochastic optimization problem as a two-stage continuous-state Markov decision process, with a sensing-and-probing stage and a transmit-power-control stage. We simplify this problem by a more useful after-state value function formulation. We then propose a reinforcement learning algorithm to learn the after-state value function from data samples when the statistical distributions of harvested energy and channel fading are unknown. Numerical results demonstrate learning characteristics and performance of the proposed algorithm.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".