Online Joint Power Control for Two-Hop Wireless Relay Networks With Energy Harvesting
Bibliographic record
Abstract
We consider a two-hop amplify-and-forward relay network with energy harvesting nodes, and design online joint power control at the source and the relay to maximize the long-term time-averaged rate over fading channels. We formulate the problem as a joint stochastic optimization problem under battery operational constraints and finite storage capacity constraints. In seeking an online solution, we transform the problem into one that enables us to leverage Lyapunov optimization to develop an online algorithm to provide the joint power control solution for the source and the relay in a fading environment. The joint power control solution is derived in closed-form and only depends on the current energy arrival at each node and fading condition over each hop, without requiring any statistical knowledge of them. Our proposed algorithm not only adapts the power based on the battery energy levels to conserves energy, but also exploits opportunistic transmission based on fading condition. Through analysis, we show that the performance gap of our proposed algorithm to the optimal power control policy is bounded. Simulation results demonstrate a significant gain of our proposed online joint power control algorithm over other alternative methods, including pernode separate power control and heuristic joint power control methods.
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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 | 0.002 |
| 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 | 0.001 | 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".