Semantic-Aware Spectrum Efficiency for 6G V2x URLLC with Multi-Agent Hierarchical DRL
Bibliographic record
Abstract
In this study, we propose SCF6, a novel semantic communication framework for 6 G -enabled vehicular networks tailored to ultra-reliable low-latency communication (URLLC) scenarios. SCF6 integrates semantic encoding/decoding with conventional channel processing, optimizing transmission by focusing on data meaning. Leveraging BERT (bidirectional encoder representations from transformers)-based natural language processing, it ensures high semantic similarity between transmitted and received messages. To maximize semantic spectrum efficiency (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under strict URLLC constraints, we design a multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework. MAHASDRL coordinates resource allocation and spectrum sharing, embedding hierarchical attention at both semantic and channel levels to enhance decision-making, optimize power control, and reduce interference. Simulations demonstrate SCF6's superiority over traditional DRL methods in spectrum efficiency, reliability, and latency, proving effective for dynamic urban vehicular networks.
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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".