Multi-Encoder Semantic Communication for Human Digital Twin Synchronization
Bibliographic record
Abstract
Human digital twin (HDT) is an innovative concept that creates digital representations of humans to support human-centric services by enabling real-time data synchronization between the physical twin (PT) and the virtual twin (VT). This PT-VT synchronization process is inherently data-intensive, requiring efficient resource management, especially in resource-constrained environments. Semantic communication has emerged as a promising alternative to traditional data-driven methods. However, single-encoder models may struggle to meet the dynamic requirements of HDT applications, particularly when resources are limited. To address these challenges, this paper introduces a multi-encoder semantic communication model that dynamically allocates resources—such as bandwidth and computational frequency—on specific application demands. The framework is formulated as a mixed-integer nonlinear programming (MINLP) problem and is solved using a genetic algorithm (GA) based approach. Simulation results demonstrate that the proposed model optimizes synchronization while effectively managing power consumption, outperforming traditional single-encoder models in terms of accuracy, latency, and resource efficiency. This dynamic multi-encoder approach offers a scalable and adaptable solution for future HDT applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".