A Data-Driven Wasserstein Distributionally Robust Weight-Based Joint Power Optimization for Dynamic Multi-WBAN
Bibliographic record
Abstract
To improve the reliability of dynamic multiple wireless body area networks (WBANs) system, it is indispensable to comprehensively consider the interference mitigation and user data differences. In this paper, we study a multi-WBAN system, where sensors receive radio frequency (RF) signals from the access point (AP), then transmit the monitoring sign to the sink node. Considering the dynamic network topology and the individuality of users, we propose a data-driven wasser-stein distributionally robust weight-based joint power allocation (DW-JPA) scheme. In particular, we formulate a sum-weighted transmission rate maximization problem by optimizing dynamic weight and transmit power ratio subject to the data transmission and energy limitation constraints. We divide the problem into dynamic weight subproblem and transmission power control subproblem. We utilize the collected physiological data to predict the optimal actual weight assignment. Then, we quantify the criticality of sensors and build an ambiguity set based on wasserstein distance for probability distributions of the critically. In essence, the optimal weight is obtained by using the distributionally robust optimization (DRO) method. Furthermore, due to the non-convexity of the power control subproblem, we convert the subproblem to a difference of convex (DC) problem and use an iterative algorithm to alternately optimize the power ratio. The results reveal that the proposed scheme achieves a significantly higher weighted transmission rate with physiological data compared with traditional schemes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".