Hierarchical Attention-Based Multi-Agent DRL for Semantic-Aware Spectrum Efficiency in 6G V2X
Bibliographic record
Abstract
In this paper, we propose a novel semantic communication framework (SCF6) tailored for 6G-enabled vehicular networks, targeting ultra-reliable low-latency communication (URLLC) scenarios. By integrating semantic encoding/decoding with traditional channel processing, our framework optimizes data transmission, focusing on the meaning of the data rather than raw information. To quantify the integrity of the transmitted messages, we employ advanced natural language processing techniques, such as BERT (bidirectional encoder representations from transformers), ensuring semantic similarity between the sent and received information. We formulate an optimization problem that maximizes semantic spectrum efficiency evaluation (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under stringent URLLC constraints. The optimization solutions are solved by the multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework, which coordinates resource allocation and spectrum sharing among multiple vehicles. By incorporating hierarchical attention mechanisms at both semantic and channel levels, MAHAS-DRL enhances decision-making, optimizes transmission power, and reduces interference. Extensive simulation results demonstrate that our proposed framework outperforms traditional DRL approaches in terms of spectrum efficiency and reliability while significantly reducing transmission delays, making it ideal for dynamic urban vehicular networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".