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.
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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".