Optimal Relay Selection and Power Control for Energy-Harvesting Wireless Relay Networks
Bibliographic record
Abstract
Ambient energy harvesting (EH) has emerged as a promising technique to improve the energy efficiency and reduce the total greenhouse gas emissions for green relay networks. In this paper, we study the joint relay selection and power control problem for the decode-and-forward EH wireless relay network. In particular, the problem formulation is to maximize the end-to-end system throughput by a deadline under the limitations of data and energy storage. To solve the problem under an offline optimization framework, we decompose such an optimization problem into two subproblems: 1) the joint time scheduling and power control subproblem and 2) the relay selection subproblem. Due to the convex nature of the joint time scheduling and power control subproblem, we derive the optimal solution via the primal decomposition. Based on the obtained system throughput, we can quickly select the best relay that achieves the maximum throughput. For the practical implementation, we further design the sub-optimal online joint time scheduling and power control algorithm. Specifically, the best relay is first obtained based on the statistical knowledge of energy arrivals and channel states, and then the best relay decides the time scheduling and power control that maximizes the total throughput according to the instantaneous state of channel fading, energy arrival, and queue data in each time slot. Simulation results show that the proposed offline algorithm can guarantee the maximum system throughput. Moreover, the simulation results show that compared to the optimal offline algorithm, the sub-optimal online algorithm suffers only a small degradation in performance.
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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".