Sum-Throughput and Fairness Optimization of a Wireless Energy Harvesting Sensor Network
Bibliographic record
Abstract
This paper formulates and solves a joint-optimization problem whose objective is to maximize both the sum-throughput and fairness of a wireless powered communication network (WPCN) with radio frequency (RF) energy harvesting. An algorithm based on the multi-source blind adaptive beamforming with hybrid protocol that maximizes the sum-throughput and fairness (MS-BABF/Hybrid-STF) is proposed and analyzed. Numerical results show that the jointly optimized MS-BABF/Hybrid-STF protocol achieves a 36.7% increase in fairness compared to a single input, single output configuration in a dynamic environment. The MS-BABF/Hybrid-STF protocol also delivers an average of 19.1% fairness gain when the number of sensors is varied from 2 to 8 compared to a similar protocol that optimizes only the sum-throughput of a WPCN instead. The findings are significant for future widespread adoption of WPCN systems powered by RF energy sources in the real world by allowing fairer throughputs between sensors. In turn, the transmission of sensors' sensed data with the required quality can be supported even when there are ongoing changes in channel quality.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".