Power Control for Battery-Limited Energy Harvesting Communications
Bibliographic record
Abstract
Power control is often used to ensure efficient resource utilization in communication systems. Its role becomes even more critical in the emerging paradigm of energy harvesting communications due to the intermittency and randomness of ambient energy sources. This monograph provides a review of the fundamental power control policies and their performance analysis in the basic setting of a discrete-time battery-limited energy harvesting communication system with independent and identically distributed energy arrivals. Three different settings, namely, offline power control, online power control, and power control with lookahead, are considered, corresponding respectively to the cases with non-causal, causal, and partial non-causal knowledge of the energy arrival process. A complete characterization of the optimal offline power control policy is presented. In the online setting, the focus is placed on the greedy policy, which is optimal in the low-battery-capacity regime, and universally near-optimal policies, which include the maximin optimal policy, the fixed fraction policy, the two-piece fixed fraction policy, and the locally fixed fraction policy. Finally, power control with lookahead is introduced to bridge offline and online power control, the entire spectrum of optimal policies is characterized for Bernoulli energy arrivals, and the extension beyond the Bernoulli case is also discussed.
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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 | 0.000 | 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 | 0.002 |
| 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".