Reliable and Energy-Efficient Relay Transmission in WBANs with Wireless Power Transfer: Optimal Design with DRL
Bibliographic record
Abstract
Reliable and energy-efficient wireless transmission is critical to the success of future wireless body area networks (WBANs), as the energy storage capacity of biosensors is strictly constrained. In this article, we study the energy efficiency (EE)-maximization problem in a decode-and-forward (DF)-relay transmission system powered by wireless power transfer (WPT). Specifically, in each time slot, an access point (AP) transmits radio frequency (RF) signals to charge a biosensor and a relay, and then the biosensor transmits its collected data to the AP with the assistance of the DF relay. To ensure human safety and maintain transmission reliability, we formulate the EE-maximization problem under peak power constraints while considering transmission errors. Then, we apply a deep reinforcement learning (DRL) algorithm to train an action policy for determining near-optimal transmission parameter values for each transmission session. Through some selected numerical examples, we show that under peak power constraints, our solution can approach the performance obtained by the exhaustive search and exhibits superior performance compared to the traditional iterative algorithm. Therefore, our solution is instrumental in designing safe, reliable, and energy-efficient WBAN systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 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".