IRS-Assisted Joint Broadcast/Multicast Resource Allocation and Cooperative Perception Optimization for 6G V2X Networks
Bibliographic record
Abstract
Aiming at the coexistence challenges of highly reliable broadcast/multicast communication and high-precision cooperative sensing in 6G vehicular networking, this paper proposes an IRS-enhanced joint optimization framework. First, an IRS-assisted millimeter-wave-terahertz heterogeneous network architecture is constructed to reconfigure multi-vehicle communication links through dynamically programmable reflective units to solve the signal occlusion and interference problems in traditional multicast scenarios; second, a cooperative sensing task modeling method based on GAT is designed to fuse LiDAR point-cloud data and camera information into spatio-temporal sensing features to drive dynamic resource allocation demand; finally, the HFRL algorithm is proposed to jointly optimize the IRS phase matrix, broadcast spectrum allocation, computational offloading strategy and perception task priority. The simulation results show that the sensing accuracy has increased from 68.5% of the baseline to 90.5%, the URLLC reliability is 99.999%, and the spectral efficiency is 5.2 bps/Hz.
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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 | 0.001 | 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".