Performance Optimization of a Multi-Source, Multi-Sensor Beamforming Wireless Powered Communication Network With Backscatter
Bibliographic record
Abstract
This paper formulates and solves a multi-objective joint optimization problem where both the sum-throughput and fairness of a radio frequency (RF) energy harvesting wireless powered communication network (WPCN) are maximized using multiple beamforming hybrid access points (H-APs) and backscatter communication-enabled combination sensors. The paper proposes the multi-source, multi-sensor blind adaptive beamforming with combination sensors (MS2-BABF/combo) protocol. The protocol is analyzed to determine its performance with metrics, including WPCN sum-throughput, fairness in the achievable rates by sensors, sum-throughput and fairness tradeoff, and sensor dropout rate. Numerical results show that the MS2-BABF/combo protocol delivered up to approximately 296% increase in sum-throughput compared to the reference time-switching (TS) RF energy harvesting protocol in a dynamic environment, achieved a dropout rate of 0% instead of 28.5% by the reference TS protocol at 10mW H-AP transmission power, and increased Jain's fairness index from J = 0.76 to up to 0.90 and 0.80 with and without sensors operating in backscatter mode, respectively. The findings are significant for future widespread adoption of WPCN systems by increasing its performance thus the versatility of applicable scenarios in the real world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".