Multi-Objective Optimization for Energy-Efficient and Reliable Transmission in WBANs: Session-Specific Design Using MODRL
Bibliographic record
Abstract
In wireless body area networks (WBANs), ensuring energy efficiency while maintaining reliable transmission poses a significant and inherently conflicting design challenge. This paper presents a multi-objective optimization (MOO) design for wireless transmission in the finite blocklength (FBL) regime within a WBAN environment, aimed at minimizing both energy consumption and packet error rate (PER) for each transmission session. We propose to train an intelligent agent that can determine near-optimal transmission parameter values for different user preferences. Specifically, we reformulate the MOO problem as a multi-objective Markov decision process (MOMDP) and develop a multi-objective deep reinforcement learning (MODRL)-based solution to approximate the Pareto front, where the penalty-based boundary intersection (PBI) approach is utilized for reward scalarization. Simulation results demonstrate that our proposed solution can adapt transmission parameter values for each transmission session to balance energy efficiency and reliability requirements based on user preferences. Moreover, compared to the linear scalarization method, our solution with the PBI scalarization approach provides a more complete Pareto front.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".